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 ACCESSING INDIVIDUAL 
 RECORDS FROM 
 PERSONAL DATA FILES 
 USING NON-UNIQUE 
 IDENTIFIERS 
 
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 NBS Special Publication 500-2 
 
 U.S. DEPARTMENT OF COMMERCE 
 National Bureau of Standards 
 
NATIONAL BUREAU OF STANDARDS 
 
 The National Bureau of Standards 1 was established by an act of Congress March 3, 1901. 
 The Bureau's overall goal is to strengthen and advance the Nation's science and technology 
 and facilitate their effective application for public benefit. To this end, the Bureau conducts 
 research and provides: (1) a basis for the Nation's physical measurement system, (2) scientific 
 and technological services for industry and government, (3) a technical basis for equity in trade, 
 and (4) technical services to promote public safety. The Bureau consists of the Institute for 
 Basic Standards, the Institute for Materials Research, the Institute for Applied Technology, 
 the Institute for Computer Sciences and Technology, and the Office for Information Programs. 
 
 THE INSTITUTE FOR BASIC STANDARDS provides the central basis within the United 
 States of a complete and consistent system of physical measurement; coordinates that system 
 with measurement systems of other nations; and furnishes essential services leading to accurate 
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 and commerce. The Institute consists of the Office of Measurement Services, the Office of 
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 for Radiation Research: Nuclear Sciences; Applied Radiation — Laboratory Astrophysics - 
 — Cryogenics - — Electromagnetics 2 — Time and Frequency 2 . 
 
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 and provides technical advice and services to Government agencies upon request. The Insti- 
 tute consists of the following divisions and Centers: 
 
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 Product Technology: Product Systems Analysis; Product Engineering — Center for Building 
 Technology: Structures, Materials, and Life Safety; Building Environment; Technical Evalua- 
 tion and Application — Center for Fire Research: Fire Science; Fire Safety Engineering. 
 
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 tiveness in the conduct of their programs through the selection, acquisition, and effective 
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 Computer Services — Systems and Software — Computer Systems Engineering — Informa- 
 tion Technology. 
 
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 1 Headquarters and Laboratories at Gaithersburg, Maryland, unless otherwise noted; mailing address 
 Washington, D.C. 20234. 
 
 - Located at Boulder, Colorado 80302. 
 
o. 
 
 <3 
 
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 c3 
 
 COMPUTER SCIENCE & TECHNOLOGY: 
 
 Accessing Individual Records 
 From Personal Data Files 
 Using Non-Unique Identifiers 
 
 Gwendolyn B. Moore 
 John L. Kuhns 
 Jeffrey L. Trefftzs 
 Christine A. Montgomery 
 
 Operating Systems, Inc. 
 21031 Ventura Boulevard 
 Woodland Hills, California 91364 
 
 Prepared for the 
 
 Institute for Computer Sciences and Technology 
 
 National Bureau of Standards 
 
 Washington, D.C. 20234 
 
 ^ W ^ ™ 
 
 U.S. DEPARTMENT OF COMMERCE 
 
 Dr. Betsy Ancker-Johnson, Assistant Secretary for Science and Technology 
 NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director 
 Issued February 1977 
 
Reports on Computer Science and Technology 
 
 The National Bureau of Standards has a special responsibility within the Federal 
 Government for computer science and technology activities. The programs of the 
 NBS Institute for Computer Sciences and Technology are designed to provide ADP 
 standards, guidelines, and technical advisory services to improve the effectiveness of 
 computer utilization in the Federal sector, and to perform appropriate research and 
 development efforts as foundation for such activities and programs. This publication 
 series will report these NBS efforts to the Federal computer community as well as to 
 interested specialists in the academic and private sectors. Those wishing to receive 
 notices of publications in this series should complete and return the form at the end 
 of this publication. 
 
 National Bureau of Standards Special Publication 500-2 
 
 Nat. Bur. Stand. (U.S.), Spec. Publ. 500-2,203 pages (Feb. 1977) 
 CODEN: XNBSAV 
 
 Library of Congress Cataloging in Publication Data 
 
 Main entry under title: 
 
 Accessing individual records from personal data files using non- 
 unique identifiers. 
 
 (Computer science & technology) (NBS special publication ; 500-2) 
 
 Supt. of Docs, no.: CI 3. 10:500-2 
 
 1. Personnel records— Data processing. 2. Civil service— United 
 States— Personnel management. I. Moore, Gwendolyn B. II. Institute 
 for Computer Sciences and Technology. III. Series. IV. Series: United 
 States. National Bureau of Standards. Special publication ; 500-2. 
 QC100.U57 no. 500-2 [JK.766.5] 602Ms [353.001] 76-57950 
 
 U.S. GOVERNMENT PRINTING OFFICE 
 WASHINGTON: 1977 
 
 For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C 20402 
 (Order by SD Catalog No. C13:10:500-2). Stock No. 003-003-01726-4 Price $2.65 
 (Add 25 percent additional for other than U.S. mailing). 
 
FOREWORD 
 
 How does a Federal agency locate a personal record in one of its 
 massive files with only a limited amount of information for guidance? 
 The Privacy Act of 1974 presented numerous agencies with just this 
 problem when it specified: "It shall be unlawful for any Federal, 
 state, or local government agency to deny to any individual any 
 right, benefit, or privilege provided by law because of such 
 individual's refusal to disclose his Social Security account number." 
 
 Those Federal agencies denied the use of Social Security numbers are 
 not the only ones interested in the solution to this problem. For 
 instance, one might be required to locate individuals in a file 
 who have been inadvertently or illegally recorded under more than 
 one account number. Similarly, the results of a study of 
 methodologies for retrieving an individual's record without the 
 use of a universal identifier can be used to determine the threats 
 to an individual's privacy. 
 
 This report was written in response to this growing need for 
 retrieving information using non-unique identifiers. Presented are 
 selected methodologies for assisting Federal agencies in selecting 
 retrieval algorithms and name lookup techniques; in analyzing their 
 data by the identification of weighting factors and statistical 
 sampling for determining error and omission rates; and lastly, 
 predicting the accuracy and efficiency of candidate retrieval keys. 
 
 Seymour Jeffery, Chief 
 Systems and Software Division 
 Institute for Computer Sciences 
 and Technology 
 National Bureau of Standards 
 
 111 
 
ACKNOWLEDGMENTS 
 
 The authors would like to express their appreciation to all those 
 who contributed to the preparation of this document through their 
 willingness to discuss their relevant experience and expertise. 
 Particular thanks are due two members of the NBS Institute for 
 Computer Sciences and Technology: Dr. Thomas C. Lowe, who coordin- 
 ated this effort from its inception, gave incentive and direction 
 to the project while assisting in eyery way possible with preparation 
 of the final report; and John L. Berg, whose comments and suggestions 
 have been invaluable throughout the course of the project. 
 
 IV 
 
TABLE OF CONTENTS 
 
 Page 
 
 1.0 Introduction 1 
 
 1.1 The Legislation 1 
 
 1.2 Retrieval of Individual Records 3 
 
 1.3 Statistical vs. Dossier Files 4 
 
 1.4 Privacy vs. Security 6 
 
 1.5 Summary 8 
 
 2.0 State-of-the-Art Retrieval Techniques 9 
 
 2.1 Introduction . 9 
 
 2.2 Varying Selective Values of Non-Unique Identifiers ... 10 
 
 2.3 Importance of the Name Variable in Retrieval 
 
 Through Use of Non-Unique Identifiers 12 
 
 2.4 Selected State-of-the-Art for Name Lookup Techniques . . 12 
 
 2.5 Implementation of Name Lookup Techniques 26 
 
 2.6 Other Search Strategies 29 
 
 3.0 Identification of Weighting Factors 32 
 
 3.1 Accuracy Constraints Imposed by the Privacy Act 32 
 
 3.2 Determination of Weighting Factors for Existing Files. . 35 
 
 3.2.1 Data Base Definition and Forms Design 36 
 
 3.2.2 Data Collection Techniques 37 
 
 3.2.3 Keying of Data and Input Controls 42 
 
 3.2.4 Computer Edit Routines 44 
 
 3.2.5 Manual Checks for Accuracy 47 
 
 3.3 Summary of Weighting Factors 49 
 
 4.0 File Validation 52 
 
 4.1 Introduction 52 
 
 4.2 Validation Techniques 52 
 
 4.3 File Validation Example 53 
 
 5.0 Computer Simulation of Data Base Retrieval 59 
 
 5.1 Precision and Recall 59 
 
 5.2 Query Formulation for Best Results 61 
 
 5.3 Soft-Match Techniques 62 
 
 5.4 Example of Use of File Validation Summary Data ..... 62 
 
 5.5 Precision and Probable Match ........ 66 
 
 6.0 Conclusions and Recommendations 69 
 
 6.1 Conclusions 69 
 
 6.2 Recommendations 70 
 
 6.2.1 Evaluation of Current File Access Capability. . . 71 
 
 6.2.2 Evaluation of the Data Base 71 
 
 Appendix I - Precision Tables 73 
 
TABLE OF ILLUSTRATIONS 
 
 Page 
 
 Table 2.4-1 IBM Alpha Inquiry System Personal 
 
 Name Encoding Algorithm 15 
 
 Table 2.4-2 Western Airlines Surname Match Rating Algorithm. ... 18 
 
 Table 2.4-3 Rules for Developing Phonetic Frequency Codes 19 
 
 Table 2.4-4 Letter Distributions for Phonetic Frequency 
 
 Code Name Lookup Technique 21 
 
 Table 2.4-5 Illustration of the Application of the NYSIIS 
 
 Coding Techniques 22 
 
 Table 2.4-6 NYSSIS Name Coding Rules 23 
 
 Form 3.1-1 Decay Factors of Standard Biographical 
 
 Data Elements 34 
 
 Form 3.2.1-1 Inherent Characteristics of Biographical 
 
 Data Elements 38 
 
 Form 3.2.1-2 Data Base Definition Weighting Factors for 
 
 Biographical Data Elements ..... 39 
 
 Form 3.2.2-1 Data Collection Procedure Weighting Factors for 
 
 Biographical Data Elements 43 
 
 Form 3.2.3-1 Keying and Input Control Weighting Factors 
 
 for Batch Input . 45 
 
 Form 3.2.3-2 Keying and Input Control Weighting Factors 
 
 for Interactive Input 46 
 
 Form 3.2.4-1 Computer Editing Weighting Factors 48 
 
 Form 3.2.5-1 Weighting Factors for Manual Accuracy Checks 50 
 
 Form 3.3-1 Cumulative Weighting Factors for Standard 
 
 Biographical Data Elements 51 
 
 Table 4.2-1 Suggested Format for File Validation Record 54 
 
 Table 4.3-1 Source Documents for Ten Sample Records from 
 
 the Dummy Data Base. ............ 55 
 
 Table 4.3-2 Computer Listings for Ten Sample Records from 
 
 the Dummy Data Base 56 
 
 Table 4.3-3 File Validation Record for a Sample Record 
 
 from the Dummy Data Base 57 
 
 Table 4.3-4 Summary of Errors and Omissions Detected in 
 
 Analysis of 10% of the File. 58 
 
 Table 5.3-1 Retrieval Ratios (Estimates of Proportion of File 
 
 Retrieved) 63 
 
 Table 5.3-2 Statistical Classification of Surname Records as 
 
 Compiled from Social Security Administration Accounts. 64 
 
 vi 
 
TABLE OF CONTENTS 
 (continued) 
 
 Page 
 
 Appendix II - A Probabilistic Formulation of the 
 
 Non-Unique Access Problem 174 
 
 Appendix III - Soft-Match Techniques 181 
 
 Appendix IV - FORTRAN Source Code for the Probability Mode. ... 186 
 
 VI 1 
 
Digitized by the Internet Archive 
 in 2013 
 
 http://archive.org/details/accessingindividOOmoor 
 
ACCESSING INDIVIDUAL RECORDS FROM 
 PERSONAL DATA FILES USING NON-UNIQUE IDENTIFIERS 
 
 G. B. Moore, J. L. Kuhns, J. L. Trefftzs, C. A. Montgomery 
 Operating Systems, Inc. 
 
 ABSTRACT 
 
 The Privacy Act of 1974 places restrictions on the Federal, state, and 
 local agencies' use of the Social Security account number as an identi- 
 fier. For some agencies, compliance will involve changes in implementa- 
 tion of retrieval algorithms. This report describes methodology applic- 
 able to these changes in the more general context of the problem of 
 retrieving individual records from files using non-unique identifiers. 
 State-of-the-art retrieval techniques are discussed, a method for assign- 
 ing reliability weights to various personal data elements is presented, 
 file validation techniques for the error and omission rates of data items 
 are suggested, and a retrieval probability model -- designed to show 
 likelihood of retrieval of a subject's record given a variety of popula- 
 tions, combinations of identifiers, and error/omission rates -- is des- 
 cribed. A methodology is developed for forming confidence factors from 
 the established error/omission rates for combinations of non-unique 
 identifiers that are candidates for use as retrieval keys. Use of these 
 confidence factors as indices into the precision tables produced by the 
 probability model is described. 
 
 Key Words: Data retrieval; file validation; name lookup; non-unique 
 
 identifiers; personal data files; Privacy Act, probability 
 model ; retrieval . 
 
 1.0 INTRODUCTION 
 1. 1 The Legislation 
 
 The Privacy Act of 1974 opens with the words: 
 "The Congress finds that- 
 
 (1) the privacy of an individual is directly affected by the 
 collection, maintenance, use, and dissemination of personal 
 information by Federal agencies; 
 
 (2) the increasing use of computers and sophisticated informa- 
 tion technology, while essential to the efficient operations 
 of the Government, has greatly magnified the harm to indi- 
 vidual privacy that can occur from any collection, main- 
 tenance, use, or dissemination of personal information; 
 
 (3) the opportunities for an individual to secure employment, 
 insurance, and credit, for his right to due process, and 
 other legal protections are endangered by the misuse of 
 certain information systems; 
 
 1 
 
(4) the right to privacy is a personal and fundamental right 
 protected by the Constitution of the United States; and 
 
 (5) in order to protect the privacy of individuals identified 
 "in information systems maintained by Federal agencies, it 
 
 is necessary and proper for the Congress to regulate the 
 collection, maintenance, use, and dissemination of infor- 
 mation by such agencies."* 
 
 There has been increasing public concern over the trend toward integra- 
 tion and centralization of data banks containing personal information. 
 In the privacy legislation, Congress has explicitly defined the rights 
 of citizens whose records are maintained by Federal agencies as well as 
 the responsibilities of the agencies handling the data. In addition, 
 the legislation also established the Privacy Protection Study Commission 
 which is specifically authorized to: 
 
 "...make a study of the data banks, automated data processing 
 programs, and information systems of governmental, regional, 
 and private organizations, in order to determine the standards 
 and procedures in force for the protection of personal infor- 
 mation... In the course of conducting the study... the Commis- 
 sion may research, examine, and analyze. . .the use of social 
 security numbers, license plate numbers, universal identifiers, 
 and other symbols to identify individuals in data banks and to 
 gain access to, integrate, or centralize information systems 
 and files." 2 
 
 Specific prohibitions regarding use of universal identifiers will, for 
 the most part, await the results of the Privacy Protection Study 
 Commission study. The current emphasis is rather on restricting the 
 data maintained to that directly relevant to the agency. However, 
 si-nce much of the concern over use of universal identifiers has been 
 generated by the use of the Social Security account numbers and their 
 potential for linking files, the Act has specifically provided against 
 its use as a required item of information unless a Federal statute 
 specifying such a disclosure is applicable. 
 
 "It shall be unlawful for any Federal, State or local govern- 
 ment agency to deny to any individual any right, benefit, or 
 privilege provided by law because of such individual's refus- 
 al to disclose his social security account number." 3 
 
 P rivacy Act of 1974 , Public Law no. 93-579, Section 1 
 
 2 
 Loc. cit. 
 
 3 
 Ibid. , Section 7. 
 
Thus the effect of the legislation is that individuals whose records are 
 maintained in the files of Federal agencies must be allowed to review 
 their own records on request, the retrieval must be possible without use 
 of the Social Security account number, the data maintained in the files 
 must be timely, and only data necessary in the performance of the agency's 
 mission should be stored. 
 
 For agencies that have relied heavily on the Social Security number for 
 purposes of retrieval, this requirement will necessitate some major re- 
 adjustments. Further restrictions are imposed on the type of information 
 that can be solicited from individuals who have requested copies of their 
 record by the July 1, 1975 guidelines from the Office of Management and 
 Budget, "Guidelines for Implementing Section 3 of the Privacy Act of 
 1974," which states that the items published in the Federal Register by 
 each agency must include: 
 
 "...the information necessary to identify the record. Where 
 the system employs a specialized identification scheme, the 
 individual should not be required to provide such a number or 
 symbol as an absolute requirement, although the individual 
 might be requested to supply it if he or she can reasonably 
 be expected to know it. Instead, alternative combinations 
 of personal characteristics may be used to identify individ- 
 uals who may have lost, forgotten, or are unaware of their 
 identification numbers or symbols. For example, the combina- 
 tion of name, date of birth, or place of birth, and father's 
 first name may be sufficient to identify an individual with- 
 out the use of a system identification number. "4 
 
 Thus, although use of record identification numbers, driver's license 
 numbers, etc., is not prohibited, careful attention must be given to the 
 selection of a set of identifiers that will be considered reasonable in 
 the particular environment. 
 
 1.2 Retrieval of Individual Records 
 
 The implications of these restrictions will differ from agency to agency. 
 It is important, however, that those charged with responsibility for the 
 selection and implementation of identification schemes be aware of the 
 retrieval effectiveness of non-unique personal identifiers. The potential 
 for retrieval by means of exclusive use of combinations of these keys 
 should be understood, and the relevance of this potential for use with 
 currently active data bases should be examined. Consideration must also 
 be given to the question of the reliability of the data contained in 
 these files since the success of the retrieval algorithm depends upon 
 the accuracy of the data base itself. Errors may have been introduced 
 into the data base (during data collection, data entry, data update, etc.), 
 
 Office of Management and Budget Circular A-108 and accompanying "Guide- 
 lines for Implementing Section 3 of the Privacy Act of 1974," Federal 
 Register , 9 July 1975, Vol. 40, No. 32, pp. 28948-28978. 
 
 3 
 
with the result that a request from an individual for a copy of his re- 
 cord might fail due to the presence of erroneous data in the record 
 itself. In this respect, retrieval algorithms based on Social Security 
 account number or other standard universal identifiers have advantages 
 (it is easier to alert clerical staff to the necessity of checking one 
 or two numbers for accuracy than it is to train them to be careful with 
 all biographical data), adequate retrieval is certainly possible using 
 non-unique identifiers such as name, birthdate, etc. In fact, such 
 retrieval is sometimes possible even when the variables most closely 
 identified with an individual are removed from the records. Statisticians 
 involved in the collection and analysis of statistical data for research 
 purposes have been concerned with the problem of protection of the in- 
 dividual whose data is maintained in the files. The need to collect 
 such data in order to study causes of disease, poverty, violence, migra- 
 tion and other sociological phenomena is well understood, but the individ- 
 uals involved have the right to expect that information given in con- 
 fidence will be treated with integrity. Dr. Fellegi of the Dominion 
 Bureau of Statistics in Canada has summarized the problem: 
 
 "The public benefits indirectly from the legitimate uses of 
 statistics by governments, businesses, non-profit organiza- 
 tions, academic users, etc.; yet, the public is also concerned 
 about the increasing burden of providing the required statis- 
 tics and about the real or imagined possibility of the misuse 
 of the data provided by them. The explosive increase in the 
 demands for more statistics can only be met, without impossible 
 response burdens being put on the public, through a more effect- 
 ive exploitation of the data. This increases geometrically the 5 
 magnitude of the problem of checking tabulations for disclosure." 
 
 The seemingly contradictory requirements of protecting individual privacy 
 while at the same time providing the citizen the benefits attendant on the 
 introduction of automated record keeping systems create a burden for those 
 responsible for maintaining the systems. They must restrict the data 
 retained in the file to that clearly relevant to the application, but 
 at the same time be able to identify correctly all individuals concerned 
 on demand. Data must be available for research efforts, but information 
 maintained to support these efforts must be restricted and accessing of 
 individual records carefully monitored. The dilemma thus created is 
 discussed further in the next section. 
 
 1.3 Statistical vs. Dossier Files 
 
 Until recently, it was generally believed that a basic distinction ex- 
 isted between statistical files (those with names, addresses, etc., 
 removed from the individual record) and dossier files (those containing 
 explicitly descriptive information about the individuals). Dossiers 
 
 5 
 Fellegi, I. P., "On the Question of Statistical Confidentiality," 
 
 Journal of the American Statistical Association , 67 (March 1972), 
 
 p. 7-18. 
 
 4 
 
were considered dangerous from the point of view of exposure whereas 
 statistical files were not. James Martin, in discussing this distinc- 
 tion, says: 
 
 "It is important to separate the concept of statistical files 
 from the concept of dossiers about individuals. From the 
 point of view of privacy protection, they are entirely differ- 
 ent. Many of the uses to which the information in government 
 and research data banks will be put are statistical. In 
 statistical files the identification of the individual should 
 be stripped off. Once that is done the files can be used more 
 freely. It is still necessary to make sure that people cannot 
 be identified by some other means, such as home location, a 
 high salary, the fact that he has 13 children or cats, weighs 
 300 pounds, or has a combination of factors that makes him 
 unique. It is sometimes possible to extract information 
 about individuals from a statistical data bank by asking 
 combinations of questions. Sometimes a large number of 
 questions would be needed and controls can be devised to 
 prevent such interrogation. The controls are more complex 
 and subtle than on the type of data bank in which details 
 about an individual are requested directly. In general, 
 statistical data banks will not reveal the identities of 
 the persons about whom data are recorded. "^ 
 
 Recent research indicates that the data contained in statistical files is 
 not necessarily protected from unauthorized disclosure. Dr. SchloYer of 
 the Department of Medical Statistics at the University of Ulm in Germany 
 gives the following account of his investigation of retrieval from inter- 
 active data banks. 
 
 "Until fairly recently, removing name, address, and -- per- 
 haps -- date of birth from a record would secure anonymity 
 in most cases. The advent of the computer has changed this 
 situation. It was soon realized that the distinction between 
 a dossier data bank (returning full identifying information 
 to the user) and a statistical data bank (providing only 
 numbers of persons) is largely delusive, if the user is allow- 
 ed to communicate with the data bank by dialogue. 
 
 A prerequisite for this intrusion by dialogue is preknow- 
 ledge. The intruder must have some a_ priori information 
 from the statistical data bank. The same information must 
 be stored in Mr. X's record, so that preknowledge and the 
 corresponding part of the record can be matched. The pre- 
 knowledge must suffice to identify or, at least, nearly 
 identify Mr. X. An identification experiment with authentic 
 
 6 
 
 Martin, James, Security, Accuracy, and Privacy in Computer Systems 
 Prentice-Hall, New Jersey, 1973, p. 432. 
 
data will be described in this paper. It will be seen that 
 relatively little preknowledge will do for identification 
 purposes -- at least for the life 'attacked' in this experi- 
 ment.'^ 
 
 Not only does Dr. Schltirer's work make it clear that records can be re- 
 trieved through the use of non-unique identifiers, it also gives added 
 credence to the requirement that only relevant data be maintained in the 
 files, since inclusion of extraneous data makes the record more vulnerable. 
 
 Further indication of the need for such restrictions comes from the re- 
 search in record linkage carried out by Drs. Fellegi and Sunter. They 
 have developed 
 
 "...a mathematical model... to provide a theoretical framework 
 for computer-oriented solution to the problem of recognizing 
 those records in two files which represent identical persons, 
 objects or events (said to be matched) ...A comparison is to 
 be made between the recorded characteristics and values in 
 two records (one from each file) and a decision made as to 
 whether or not the members of the comparison-pair represent 
 the same person or event, or whether there is insufficient 
 evidence to justify either of these decisions at stipulated 
 levels of error... A theorem describing the construction and 
 properties of the optimal linkage rule and two corollaries 
 to the theorem which make it a practical working tool are 
 given. "8 
 
 The existence of such techniques for linking individual records on the 
 basis of non-unique identifiers again points up the feasibility of imple- 
 menting retrieval algorithms based on those variables. An awareness of 
 the availability of such techniques further clarifies the nature of the 
 threats that currently exist to individual privacy. It also emphasizes 
 the importance of the provisions for relevancy of data in the Privacy 
 Act since obviously the more data available, the easier such linkages 
 become. Similarly, the necessity for building audit checks into the 
 system to provide a means for determining what information was accessed 
 by whom becomes clear in the light of currently available methodologies. 
 
 1.4 Privacy vs. Security 
 
 Privacy has been perceptively defined by Westin as the right "...to g 
 determine what information about ourselves we will share with others,"' 
 
 7 
 Schlbrer, J., "Identification and Retrieval of Personal Records from a 
 
 Statistical Data Bank," Methods of Information In Medicine , 14 (1, 1975). 
 
 o 
 
 Fellegi, I. P., and A. B. Sunter, "A Theory for Record Linkage," Journal 
 of the American Statistical Association , (Dec. 1969), pp. 1183-1210. 
 
 9 
 Westin, Alan, as quoted by I. P. Fellegi, "On the Question of Statistical 
 
 Confidentiality," op.cit. , p. 18. 
 
 6 
 
where the 'others' are persons or agencies who are presumably authorized 
 to hold and use such information. Thus the legislative concept of pri- 
 vacy in effect concerns the protection of information about individuals 
 from misuse by duly constituted authorities, or persons or agencies to 
 which such authority has been delegated (e.g., for statistical studies). 
 
 Security, on the other hand, is concerned with protection of this infor- 
 mation from unauthorized 'others.' Such unauthorized access may be de- 
 liberate, or inadvertent: e.g., a person seeking information from his own 
 record may be given the dossier of another individual by mistake. 
 
 Protection of information in a computerized file from unauthorized access 
 is partly a technical, system-internal, matter, and partly a procedural, 
 system-external matter. System-internal protections include obvious items 
 such as use of passwords and other identifying techniques to guard against 
 deliberate access attempts by unauthorized persons. More fundamentally, 
 such protection requires a high degree of precision in the retrieval al- 
 gorithm to prevent inadvertent delivery of the wrong information record 
 to a particular requestor (as well as erroneous updates and purges). 
 
 Procedural protections involve basic items such as locks and security 
 guards, as well as requests for proof of identity from persons seeking 
 information about their own records, further verification based on ques- 
 tions about the content of the record before it is delivered to the re- 
 questing individuals, and so forth. In the case of a computerized infor- 
 mation bank where requesting individuals are allowed access via a computer 
 terminal, a combination of technical and procedural safeguards is required 
 to insure protection of the component information records. 
 
 In a recent article on computer privacy and computer security, Willis 
 Ware states: 
 
 "In the context of computer-based systems, the matter of access 
 control is an essential part of a larger issue referred to as 
 computer security which can be defined as: 
 
 The protection of the equipment, facilities and data 
 of an information system against deliberate or acci- 
 dental damage, and against denial of use by legitimate 
 users, together with the assurance that information 
 will be delivered by the system only to individuals 
 authorized to receive it."^ 
 
 The phrase "legitimate users" of an information system implies in the 
 privacy context that legitimate uses of personal information can be de- 
 fined, and that "individuals authorized" to access such information will 
 respect the right to privacy of those persons who provided it. This is 
 
 Ware, Willis H. , "Computer Privacy and Computer Security," Bulletin of 
 the American Society for Information Science , Vol. 1, No. 3, Oct. 1974. 
 
 7 
 
the core of the privacy issue, which essentially involves the loss of 
 control over personal information incurred by an individual in providing 
 such data to some agency or data bank, and the profound effect that re- 
 linquishing control of this information can have on the life of the 
 particular individual. It is the concern of those involved in the effort 
 to protect the privacy of the individual to identify measures, legal, 
 procedural, and technological, by which potentially detrimental effects 
 of this loss of control can be nullified. 
 
 1.5 Summary 
 
 In Section 1 some of the difficulties faced by those responsible for the 
 management of personal data bases in Federal agencies are outlined. Pro- 
 blems inherent in the provision of continued service with no attendant 
 reduction in efficiency due to the increased emphasis on the protection 
 of individual privacy have been stressed. The remainder of this report 
 is concerned with the presentation of methodologies potentially useful 
 in the minimization of these difficulties. 
 
 The application of current retrieval technologies to the problem of 
 retrieval through use of non-unique personal identifiers is the basic 
 methodology under consideration. A discussion of relevant retrieval 
 algorithms is given in Section 2. Section 3 suggests an approach to 
 determination of the accuracy and reliability of the various identifiers 
 contained in current files. Forms that might be used in carrying out an 
 analysis procedure are introduced, and a mechanism for arriving at 
 weighting scores for the individual identifiers is described. Section 4 
 demonstrates a similar mechanism for analyzing error and omission rates 
 for each of the personal identifiers contained in the file. 
 
 Section 5 describes the probability model developed to assist in deter- 
 mination of the best combination of personal identifiers available for 
 use as retrieval keys given the idiosyncratic error and omission rates 
 of those variables within a particular file. An example of the use of 
 the output from the probability model in making this determination is 
 given. Conclusions are formulated in Section 6 which also contains a 
 summary of procedures recommended for those involved in the analysis of 
 personal data files. 
 
 Four Appendices are included: 
 
 Appendix I: Precision Tables 
 
 Appendix II: A Probabilistic Formulation of the Non-Unique Access 
 
 Problem 
 Appendix III: Soft Match Techniques 
 Appendix IV: FORTRAN Source Code for the Probability Model. 
 
2.0 STATE-OF-THE-ART RETRIEVAL TECHNIQUES 
 
 2.1 INTRODUCTION 
 
 The personal data files affected by the provisions of the Privacy Act 
 represent a wide spectrum of applications with very different require- 
 ments. By definition the files all contain dossier-type data, but they 
 vary in terms of the non-biographical variables maintained, the ulti- 
 mate use of the data, the frequency of file access, the retrieval 
 mechanisms employed, the degree to which accuracy has been stressed, 
 etc. These distinctions reflect the differing operational requirements 
 of the agencies involved. Clearly, the provisions of the Privacy Act 
 giving the individual access to his own records will cause re-evaluation 
 of file management requirements and, in many cases, some system changes 
 will be necessary in order to make this data available. 
 
 A clear-cut requirement for some system changes exists when the retrieval 
 keys do not include any of those combinations of identifiers that can be 
 readily recalled by the individual. Similarly, systems that have placed 
 a high degree of reliance on the use of the Social Security account 
 number in the retrieval algorithms will require extension of these system 
 modifications to insure effective retrieval. But even if these changes 
 result in the availability of a standard set of biographical variables 
 as potential retrieval keys, the level of accuracy maintained for those 
 variables may fall short of that required of retrieval keys. It is 
 entirely possible that a higher degree of accuracy exists for the vari- 
 ables that have historically been used as retrieval keys than for those 
 now promoted as candidates for that task. Such a disparity in levels of 
 accuracy could have resulted from awareness on the part of the clerical 
 staff of the need to verify the retrieval keys, or from the programming 
 of special checks on those fields, or the like. Further, this skewing 
 of levels of accuracy may well have gone unnoticed since the retrieval 
 algorithm has operated sufficiently well to fulfill the operational 
 requirements of the agency. 
 
 To illustrate this point, consider the case where an agency uses a 
 specific data item, for example AGE, to trigger notification of the 
 individual concerning a change in status. In implementing and maintain- 
 ing such a system, the emphasis on manual and system checks of the vari- 
 ables BIRTHDATE and AGE would be greater than that on NAME and ADDRESS. 
 Indeed, since postmen have become expert at detecting near matches on 
 name and street numbers, the degree of accuracy required to complete 
 the correspondence is indeed less than that required for retrieval of 
 the record on the basis of current age. 
 
 One of the authors, Mr. Trefftzs, has experienced this phenomenon. His 
 is an unusual name that causes a great deal of confusion. As a result 
 it is seldom spelled correctly. Mr. Trefftzs has, in recent weeks, 
 received communications from a variety of sources (governmental, maga- 
 zines, professional societies, etc.) in which his name was consistently 
 
 9 
 
misspelled in a number of inconsistent ways: 
 
 TREVITZ TRAFFT TRAFFTZS TRRFFTZA TREFTZ etc. 
 
 These misspellings prevented neither the retrieval of his record nor 
 the delivery of the message. Yet, if the systems involved were capable 
 of handling retrieval on the basis of biographical data including name, 
 and he were to request a copy of his record from each, it is likely 
 that some of the systems would fail to locate the record, given the 
 nature of the error that exists in the name itself. This emphasizes 
 the importance of the change of system perspective attendant on intro- 
 duction of new accessing requirements. Problems such as gross mis- 
 spellings of names may block retrieval of certain records until the 
 level of accuracy of the NAME variable has been brought within reason- 
 able bounds. 
 
 Existing file organizations and retrieval mechanisms may thus be entirely 
 responsive to the needs of a particular agency while being inadequate in 
 terms of handling the new requirements. The most difficult aspect of 
 this problem arises from the fact that the required retrieval capabili- 
 ties may not have been built into the original system. In some cases, 
 extension of current system capabilities will be sufficient to handle 
 the new requirements; in others, new sets of capabilities will be needed; 
 still other situations will arise where restructuring of the files is 
 required in order to make this data available in the required way. 
 
 Decisions regarding the system changes required will be based on several 
 factors: demand (i.e., the number of requests for this information), 
 capabilities of the current system (some will be set up in such a way 
 that required changes will be minimal), characteristics of the data base 
 (number of non-unique identifiers, available accuracy rates, typical 
 problems with a given variable), etc. If it is evident that the complete 
 redesign of the system is required in order to meet the new regulations, 
 the agency will want to examine data base structures and retrieval 
 techniques that might assist in optimizing the new system. The re- 
 mainder of this section is devoted to a description of the current 
 state-of-the-art for retrieval techniques as they apply to the problem 
 of accessing data using non-unique identifiers. Since there is no 
 single answer to the implementation of these requirements, this section 
 is presented in the form of a survey in the hope that the availability 
 of this information will clarify the options available to those con- 
 fronted with the Droblem. 
 
 2.2 VARYING SELECTIVE VALUES OF NON-UNIQUE IDENTIFIERS 
 
 Some of the identifiers available in personal data files are highly 
 individualized. The variable NAME is the most obvious example of this. 
 Other data items for example, SEX, are binary, and therefore not particu- 
 larly useful in identifying an individual record. Yet collection of 
 even the most straightforward items can be difficult since the items 
 requested are sometimes subject to vastly different interpretations. 
 
 10 
 
There is a natural tendency to assume that basic biographical data is 
 easily obtainable, and that standard definitions can be taken for grant- 
 ed. However, in actual data collection situations, surprising misunder- 
 standings can and do occur and, most importantly, are often not detected 
 until much later. The result is that variables considered to be reliable 
 in the file are many times, in reality, only marginally so. 
 
 An interesting discussion of the problems inherent in obtaining biograph- 
 ical data is given in the Uniform Hospital Discharge Data Demonstration 
 Summary Report : 
 
 "DATE OF BIRTH: Date of birth is usually an easy item 
 to collect on admission. However, there are instances 
 of elderly persons not remembering their exact date of 
 birth, and others who provide incorrect dates for per- 
 sonal reasons. 
 
 SEX: The incidence of encountering a patient with true 
 indeterminate sex status is wery low. Most hospitals 
 feel the indeterminate category is unnecessary, but 
 hospitals who perform trans-sexual operations find it a 
 useful item. 
 
 MARITAL STATUS: While this item is usually easy to ob- 
 tain, there are some inherent difficulties in assuring 
 validity, and these include: 
 
 Persons living in common-law relationships who do 
 not wish to be classified under any of the alterna- 
 tives. 
 
 Divorced persons who consider themselves single 
 and state so at admission. 
 
 Single females admitted for childbirth do not 
 generally want to be identified as single and state 
 they are married. 
 
 The original intent of this item was to attempt to 
 secure some indication of the living arrangement 
 of a patient, but it proves to be of marginal utili- 
 ty for this purpose. This is a complex variable 
 to establish if the many socio-economic factors 
 which enter into the underlying health care implica- 
 tions associated with living arrangements are con- 
 sidered. "H 
 
 11 
 
 Hodgson, D. A., L. E. Kucken, and J. M. Ensign, The Uniform Hospital 
 Discharge Data Demonstration, Summary Report , Health Service Foundation 
 Chicago, 1973, pp. 30-31. 
 
 11 
 
2.3 IMPORTANCE OF THE NAME VARIABLE IN RETRIEVAL THROUGH USE OF 
 NON-UNIQUE IDENTIFIERS 
 
 Use, as well as inherent variability, of identifiers will vary greatly 
 from one application to the next. The variability within groups of 
 identifiers is probably most extreme for the NAME variable. The fol- 
 lowing example of such variability comes from a bibliographic data base 
 application. The report states that: 
 
 "...many of the individual data elements exhibit great 
 variety (i.e., lists of their contents are extensive), 
 and show relatively disparate distributions. This be- 
 havior is encountered in different degrees in regard 
 to items such as words in the titles of monograph or 
 periodical articles, assigned subject headings, authors' 
 names and citations. ...In general, the distributions 
 are approximately hyperbolic, so that a small portion 
 of items may account for a substantial proportion of 
 occurrences, while the majority of items occur only 
 infrequently. -...Of all the data elements, personal 
 author names exhibit a distribution which is at its 
 most extreme in one direction. As is shown ...the 
 most frequent (full name) author name in a file of 
 50,000 names occurred only sixteen times, while over 
 35,000 of the names, or over 70 percent of the file, 
 occurred once only. "12 
 
 The same tables show that, with respect to surnames only 20,000 or 40% 
 of the file, occurred only once. These figures emphasize the high 
 selective value of NAME as an identifier and thus provide the rationale 
 for the following consideration of name lookup techniques. 
 
 2.4 SELECTED STATE-OF-THE-ART NAME LOOKUP TECHNIQUES 
 
 A majority of the name lookup algorithms currently in use are based on 
 a phonetic scheme aimed at minimizing the common transcription problems 
 in the recording of names. The IBM Alpha Search Inquiry Program des- 
 cription states that: 
 
 "Using the phonetic approach for personal name increases 
 
 the user's ability to access the alpha search record 
 
 even though the exact spelling of a name may not be known. 
 
 12 
 
 D. W. Fokker and M. F. Lynch, "Application of the Variety-Generator 
 Approach to Searches of Personal Names in Bibliographic Data Bases- 
 Part I. Microstructure of Personal Authors' Names," Journal of 
 Library Automation , 7 (2 June 1974), pp. 105-117. 
 
 12 
 
Thus the effects of transcription errors, partially 
 illegible signatures on correspondence, and sound- 
 alike names can be minimized."^ 
 
 In analyzing name lookup techniques, it is useful to refer to the in- 
 formation systems concepts of precision and recall. Recall represents 
 the number of relevant items contained in a given file which are re- 
 trieved in response to a particular query, while precision refers to 
 the number of file items retrieved which are actually relevant to the 
 query or search prescription. 
 
 In general, the objective of system improvement is to increase the de- 
 gree of precision or reliability, without substantially increasing the 
 amount of irrelevant material recalled or selected from the file. 
 
 In a document retrieval system, however, it is rare (in practice, at 
 least) that there is a single file item which is capable of satisfying 
 a given query. Generally speaking, several documents will be relevant 
 to a query, where the degree of relevance is defined concretely in terms 
 of the concepts used in the query and abstractly in terms of actual 
 utility to the requestor. 
 
 Conversely, in a name retrieval system, the usual case is that there is 
 a unique file item which satisfies a query, although the existence of 
 other items listed under alternate spellings of the name must also be 
 assumed by the search strategy. This is a critical issue, which does 
 not appear to be effectively handled by many of the name search tech- 
 niques investigated in the course of this study. 
 
 In evaluating name lookup techniques for which complete documentation is 
 available, there are two essential criteria for determining relative 
 effectiveness. Both of these may seem obvious, but since several of the 
 lookup techniques described here fail on one or both counts, the topic 
 merits discussion. 
 
 The first basic question is simply: does the strategy allocate similar 
 names to the same logical group -- and more particularly, are alternate 
 versions of the same name (Heinz, Heintze) in the same logical group? 
 The answer is often in the negative, and this is the primary means of 
 effecting an improvement in reliability of a given method, as we shall 
 consider below. 
 
 The second basic evaluation criterion is the converse of the first: 
 does the strategy allocate dissimilar names to different logical groups? 
 Although the possibility of violating this principle appears unlikely, 
 many name lookup techniques do in fact fail to satisfy this criterion, 
 
 13 
 Alpha Search Inquiry System General Information Manual , GH20-1188-3, 
 
 IBM Corporation, White Plains, N.Y., April 1974, p. 7. 
 
 13 
 
including Soundex, the first widely used phonological ly based system for 
 name encoding. Most of the systems currently in use have been influenc- 
 ed by the rules established 50 years ago by Margaret Odell and Robert 
 Russell for the Soundex system. 
 
 Knuth summarizes the original Soundex rules as follows: 
 
 "1. Retain the first letter of the name, and drop all 
 occurrences of a, e, h, i, o, u, w, y in other 
 
 positions 
 
 \ 
 
 2. Assign the following numbers to the remaining letters 
 after the first: 
 
 b, f , p, v ■* 1 1 + 4 
 
 c, g,j,k, q, s,x, z + 2 m, n + 5 
 
 d, t ■* 3 r •> 6 
 
 3. If two or more letters with the same code were 
 adjacent in the original name (before Step 1), 
 omit all but the first. 
 
 4. Convert to the form "letter, digit, digit, digit" 
 by adding trailing zeros (if there are less than 
 three digits), or by dropping rightmost digits 
 (if there are more than three ).-"I4 
 
 This algorithm fails to meet the evaluation criteria described above, as 
 Knuth has pointed out: 
 
 "...the names Euler, Gauss, Hilbert, Knuth, Lloyd, and 
 Lukasiewicz have the respective codes E460, G200, H416, 
 K530, L300, L222. Of course this system will bring to- 
 gether names that are somewhat different, as well as 
 names that are similar; the same six codes would be ob- 
 tained for Ellery, Ghosh, Heilbronn, Kant, Ladd, and 
 Lissajous. And on the other hand a few related names 
 like Rogers and Rodgers, or Sinclair and St. Clair, or 
 Tchebysheff and Chebyshev, remain separate. But by and 
 large the Soundex code greatly increases the chance of 
 finding a name in one of its disguises. "15 
 
 The type of refinement that has taken place in development of name look- 
 up techniques is obvious from comparing the results of applying the rules 
 used in the IBM Alpha Search Inquiry System (Table 2.4-1) with those of 
 Soundex, using two of the examples mentioned above. 
 
 14 
 Knuth, Donald E., The Art of Computer Programming, Vol. Ill: Sorting 
 
 and Searching , Addison-Wesley, Reading, Mass., 1973, p. 392. 
 
 15 
 i5 Ibid. 
 
 14 
 
Table 2.4-1 IBM Alpha Inquiry System Personal Name Encoding 
 Algorithm 16 
 
 This routine builds a phonetic key from the last name passed by the 
 calling program. The phonetic key is 7 packed bytes (14 digits). The 
 routine uses two tables, a first character table and a basic table. 
 The first character table (see below) is used to recognize first letters 
 or letter combinations and give them significance. For example, A in 
 Armour has a value of 1 to be put in the key. A in Farmer has no value 
 and is ignored. If the first letter or letter combination does not 
 appear in the first character table, 
 position and the basic table is used 
 the record key position. 
 
 a zero is inserted in the first key 
 to code the first character into 
 
 The basic table (see below) is used to recognize letters or letter 
 combinations that are phonetically equivalent. Vowels and the letters 
 H, W, Y are ignored. Letters with the same phonetic value which are 
 adjacent to each other are treated as single letters. For example, in 
 CK and TT the second character is dropped. However: if a third charac- 
 ter has the same value as the previous two, it is retained. The re- 
 mainder of any unfilled key is filled with zeroes. 
 
 The first character table used in 
 this routine is: 
 
 The basic table used in this 
 routine is: 
 
 Letter(s) 
 
 A 
 
 E 
 
 GF 
 
 GM 
 
 GN 
 
 H 
 
 I 
 
 J 
 
 KN 
 
 
 
 PF 
 
 PN 
 
 PS 
 
 U 
 
 W (except WR) 
 
 WR 
 
 Y 
 
 Value 
 
 1 
 
 1 
 08 
 03 
 02 
 
 2 
 
 1 
 
 3 
 02 
 
 1 
 08 
 02 
 00 
 
 1 
 
 4 
 04 
 
 5 
 
 Code 
 
 Letter(s) 
 
 
 
 Z,S,CI,CY,CE,TS,TZ 
 
 1 
 
 D,T 
 
 2 
 
 N 
 
 3 
 
 M 
 
 4 
 
 R 
 
 5 
 
 L 
 
 6 
 
 J,SH,SCH,CH 
 
 7 
 
 C,G,K,Q,X,DG 
 
 8 
 
 F,V,PH 
 
 9 
 
 B,P 
 
 Alpha Search Inquiry System General Information Manual , IBM Corpora- 
 tion, White Plains, New York, GH20-1188-3, 1974, pp. 41-42. 
 
 15 
 
Table 2.4-1 IBM Alpha Inquiry System Personal Name Encoding 
 Algorithm (Continued) 16 
 
 EXCEPTIONS TO BASIC TABLE PROCESSING* 
 
 In certain situations where letters or groups of letters have multiple 
 sounds, a second and third pass through name encoding is made. 
 Example: CH Has a hard sound in Nichols, a soft sound in Chavez. 
 
 
 1st Pass 
 
 
 
 
 
 (Coded in 
 
 Record) 
 
 2nd Pass 
 
 3rd Pass 
 
 cz 
 
 70 
 
 
 6 
 
 
 
 CH 
 
 6 
 
 
 70 
 
 
 
 CK 
 
 7 
 
 
 7 
 
 6 
 
 C 
 
 7 
 
 
 7 
 
 6 
 
 K 
 
 7 
 
 
 7 
 
 6 
 
 DS 
 
 
 
 
 10 
 
 10 
 
 DZ 
 
 
 
 
 10 
 
 10 
 
 TS 
 
 
 
 
 10 
 
 10 
 
 TZ 
 
 
 
 
 10 
 
 10 
 
 Following are some examples of the results of coding personal names: 
 
 NAME HARPER 
 
 KEY 2 4 9 4 = 24940000000000 
 
 NAME COLLIER 
 
 KEY 07 5 4 = 07540000000000 
 
 NAME S C H U L T Z 
 
 KEY 06 5 = 06500000000000 
 
 NAME LIVINGSTON 
 
 KEY 05 8 2701 2= 05827012000000 
 
 ^Occurrences of the letter combinations in this table cause generation 
 of alternate phonetic keys. When this happens, the lookup algorithm 
 will use both phonetic keys in the search for potential matches. This 
 procedure alleviates many of the problems attributable to phonetic miss- 
 spellings (e.g., Ohrbock for Ohrbach). A cross-reference table is used 
 to connected sound-alike names (e.g., Lyle and Lisle) which are not 
 handled by this algorithm. ; 
 
 16 
 
SEARCH CRITERION 
 
 SURNAME 
 
 ASSIGNED VALUES 
 
 SOUNDEX 
 
 ALPHA 
 
 Assign similar 
 names to the 
 same logical group 
 
 RODGERS 
 
 R326 
 
 04740000000000 
 
 ROGERS 
 
 R262 
 
 04740000000000 
 
 Assign dissimilar 
 names to differ- 
 ent logical group 
 
 KANT 
 
 K530 
 
 02100000000000 
 
 KNUTH 
 
 K530 
 
 07210000000000 
 
 In both these cases, the Alpha Search Inquiry System has solved the pro- 
 blem. There are, however, an equally good set of examples for problems 
 that still remain. Thus, although this algorithm represents progress 
 over that used in the Soundex system, it is far from perfect. 
 
 Sometimes quite straightforward algorithms are capable of handling this 
 problem successfully for files of limited extent. The match rating 
 approach used by Western Airlines (see summary in Table 2.4-2) would 
 correctly identify RODGERS and ROGERS as a potential match. In the 
 Western system, the matching algorithm would compare RDGERS with RGRS. 
 The number of unmatched characters would be 1, the resulting similarity 
 rating would be 5, and, hence, this record would be flagged for retrieval 
 
 On the other hand, this algorithm would also consider KANT to be a 
 ly match for KNUTH, which points up the limitations of the system. 
 is used successfully in this application because only a small file 
 searched at a time. Ms. Gail Hogan, Manager of Passenger Service 
 Systems and Programming for Western, states: 
 
 "Since our reservations system is flight/date oriented 
 (i.e., we limit the scope of our search by date and/or 
 flight number), we have little difficulty in obtaining 
 an exact name match. However, if there are passengers 
 on a flight with the same surname, we display a list 
 of these to the agent. If an exact match is not made, 
 we continue processing. . .in order to compile a similar 
 surname list. "17 
 
 Another example of failure to meet the criterion of assigning dissimilar 
 names to different buckets can be given using the Standardized Phonetic 
 Frequency Code algorithm (see Table 2.4-3) as described in the NYSIIS 
 evaluation of name search techniques. 18 In this system, an attempt was 
 
 like- 
 It 
 is 
 
 17 
 
 18 
 
 Personal Communication from Gail Hogan, Manager Passenger Service 
 Systems and Programming for Western Airlines, September 15, 1975. 
 
 Taft, Robert L. , Name Search Technique , New York State Identification 
 and Intelligence System, August 1970. 
 
 17 
 
19 
 Table 2.4-2 Western Airlines Surname Match Rating Algorithm 
 
 The input surname and each of the PNI [Personal Numeric Identifiers] - 
 item surnames are encoded by this program prior to comparison. Encoding 
 is as follows: 
 
 (1) Deletion of all vowels unless the vowel is the first character of 
 the surname. 
 
 (2) Elimination of all double consonants by deleting the second contig- 
 uous usage of any consonant. 
 
 (3) Reducing all encoded names to a maximum of six characters. This 
 is done by retaining the first three and last three encoded char- 
 acters. 
 
 The lengths of each pair of encoded names (input and PNI item) are 
 examined.* If they differ in length by more than two, no similarity 
 comparison is performed. A minimum acceptable similarity rating is 
 established for each pair of encoded names as follows: 
 
 Sum of lengths is 4 or less; rating of 5 
 
 Sum of lengths is 7 or less; rating of 4 
 
 Sum of lengths is 11 or less; rating of 3 
 
 Sum of lengths is 12; rating of 2 
 
 The minimum acceptable rating establishes which PNI surnames are not 
 "similar enough" to be considered by the agent. 
 
 Comparison of encoded names is then performed. This comparison is from 
 left to right, character by character. Matching pairs of characters are 
 deleted. This comparison continues until either encoded name has no re- 
 maining characters. All unmatched characters in both encoded names are 
 packed to the right and comparison proceeds from right to left. On 
 completing these comparisons the number of unmatched characters in the 
 longer name is subtracted from a value of six with the result being the 
 similarity rating for that PNI item. 
 
 Each PNI item that has a similarity rating equal to or greater than the 
 minimum rating established for this time will be added to the Retrieval 
 Control Record [RCRJ. This program will add to the RCR until the record 
 is filled. At that point additional entries will replace other items in 
 the RCR wnich have lower similarity ratings. If no item with a lower 
 rating is found, the PNI item will be ignored. 
 
 *That is, the length of the encoded input name is compared with that of 
 the encoded name in the file. 
 
 19 
 Gail Hogan, op.cit. 
 
 18 
 
Table 2.4-3 Rules for Developing Phonetic Frequency Codes 
 
 
 
 NAME FIELD 
 
 CODE 
 
 
 
 J.KUHNS 
 G.ALTSCHULER 
 
 
 1. 
 
 In the name field, convert DK to K, 
 DT to T, SC to S, KN to N and MN to N. 
 
 J . KUHNS 
 G.ALTSHULER 
 
 
 2. 
 
 In the name field, replace multiple 
 letters with a single letter. 
 
 J.KUHNS 
 G.ALTSHULER 
 
 
 3. 
 
 Remove vowels, W, H, and Y but keep 
 the first letter in the name field. 
 
 J.KNS 
 G.ALTSLR 
 
 
 4. 
 
 The first digit of the code is obtained 
 using PFl*and the first letter of the 
 name field. Remove this letter after 
 coding. 
 
 J.NS 
 G.LTSLR 
 
 1 
 3 
 
 5. 
 
 Using the last letters of the name, 
 use Table PF3*to obtain the second 
 digit of the code. Use as many let- 
 ters as possible and remove after 
 coding. 
 
 J.N 
 G.LTSL 
 
 16 
 35 
 
 6. 
 
 The third digit is found using Table 
 PF2*and the first character of the 
 first name. Remove after coding. 
 
 N 
 LTSL 
 
 167 
 357 
 
 7. 
 
 The fourth digit is found using 
 Table PF2*and the first character 
 of the name field. If no letters 
 remain use zero. After coding remove 
 the letter. 
 
 TSL 
 
 1676 
 3579 
 
 8. 
 
 The fifth digit is found in the same 
 manner as the fourth using the re- 
 maining characters of the name field, 
 if any. 
 
 SL 
 
 16760 
 35797 
 
 *De 
 
 fined in Table 2.4-4 
 
 
 
 19 
 
made to achieve a uniform distribution of codes across a set of logical 
 groups. But in the actual implementation, some phonetically dissimilar 
 symbols were assigned the same Phonetic Frequency code value as shown 
 in Table 2.4-4. 
 
 Applying the rules shown in Table 2.4-3, the names J. Kuhns and G. 
 Altshuler are reduced to the codes 16760 and 35797, respectively. How- 
 ever, as shown by the groups of letters associated with the same codes 
 in Table 2.4-4, 'T. Vines' would also reduce to 16760, and 'J. Butler' 
 to 35797. Moreover, although these quite dissimilar names would be 
 represented by the same code, 'Kuntz' -- a variant of 'Kuhns' -- would 
 not. 
 
 Returning to the issue of coding similar names such that they are al- 
 located to the same logical group, it is clear that this principle is 
 especially critical for alternate spellings of the same name. This 
 logic is based on the assumption that non-detectable* errors in record- 
 ing the name of 'Kuhns' may write 'Kuntz' instead. Also, persons 
 changing their name to a more anglicized orthography and having file 
 records listed under both spellings should be more easily traceable 
 if additional phonological-to-orthographic coding rules were introduced. 
 
 The NYSIIS coding technique introduces a few such rules -- e.g., KN ■* N, 
 Z -> S, as well as a number of ad hoc rules to achieve greater reliabil- 
 ity for the New York State Criminal justice name inventory. However, 
 many of these rules are especially aimed at a large population of Span- 
 ish surnames and may at best not be useful for files with small popula- 
 tions of Spanish surname records. At worst, they may introduce un- 
 desirable ambiguities, as in the case of 'Risque' in Table 2.4-5, which 
 illustrates the application of the NYSIIS coding rules given in Table 
 2.4-6 to a list composed mainly of OSI employees. 
 
 Again, 'Kuhns' and 'Kuntz' would result in different codes, whereas the 
 dissimilar Chinese surnames Li, Lu, Low, Liu, Lao would all result in 
 the same code, 'L' . 
 
 There is obviously still room for improvement, via the introduction of 
 phonological-to-orthographic rules based on linguistic -- rather than 
 ad hoc -- considerations. Moreover, such rules should be developed with 
 a view to accommodating a variety of ethnic surnames used in the United 
 States without biasing the coding techniques such that rules derived to 
 handle one type of surname cause ambiguous coding of other surname types. 
 
 *This is based on the assumption that most typographical errors can be 
 detected by comparing new names to the existing name list for a file 
 and generating a computer listing of unique occurrences for manual in- 
 spection. This of course assumes an investment of computer and human 
 resources for error detection and correction. 
 
 20 
 
Table 2.4-4 Letter Distributions for Phonetic Frequency Code Name 
 Lookup Technique 
 
 PHONETIC 
 
 FREQUENCY 
 
 GROUP 
 
 
 
 
 CODE 
 
 VALUE ASSIGNED 
 
 
 
 
 
 
 1 
 
 2 
 
 3 
 
 4 5 6 
 
 7 
 
 8 
 
 9 
 
 
 S 
 
 C 
 
 F 
 
 A 
 
 L D E 
 
 G 
 
 
 
 PF1 
 
 Z 
 
 K 
 
 Q 
 V 
 
 P 
 U 
 W 
 
 B 
 
 OHM 
 
 R I N 
 
 X 
 
 J 
 T 
 
 
 
 
 S 
 
 C 
 
 F 
 
 A 
 
 D M 
 
 G 
 
 U 
 
 E 
 
 PF2 
 
 Z 
 
 K 
 
 P 
 
 B 
 
 R H N 
 
 J 
 
 V 
 
 L 
 
 
 
 Q 
 
 X 
 
 
 I 
 
 T 
 
 W 
 
 
 
 B 
 
 D 
 
 F 
 
 G 
 
 MRS 
 
 Z 
 
 
 
 PF3 
 
 C 
 K 
 
 Q 
 V 
 
 T 
 
 L 
 
 P 
 
 J 
 X 
 
 N Z 
 
 E 
 
 H 
 
 I 
 
 
 
 U 
 
 W 
 
 Y 
 
 MN 
 
 TR 
 
 DRS 
 
 STN 
 
 SN 
 
 PRS 
 
 STR 
 
 SR 
 TN 
 TD 
 
 21 
 
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 CD 
 
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 CJ 
 lO 
 
 CO 
 
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 UJ 
 
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 1— 
 
 
 CD 
 CO 
 
 <£ 
 
 CX 
 
 
 I— 
 
 Li_ 
 
 u_ 
 
 CX 
 
 1— 
 
 
 
 
 
 
 
 >■ 
 
 
 
 
 
 
 
 
 
 
 
 CSS 
 
 2 
 
 
 
 
 
 
 
 
 
 _J 
 
 < 
 
 < 
 
 
 CO 
 
 CO 
 
 >- 
 
 
 
 
 
 <c 
 
 <2_ 
 
 —I 
 
 
 <c 
 
 I-nJ 
 
 X 
 
 f— 
 
 ex 
 
 CO 
 
 X 
 
 c_> 
 
 < 
 
 ex 
 
 < 
 
 < 
 
 < 
 
 CD 
 O 
 
 X 
 C_) 
 
 —J 
 <c 
 
 2 
 
 < 
 
 CD 
 
 I— 
 
 < 
 
 < 
 U. 
 < 
 
 ex 
 
 < 
 
 a 
 to 
 
 2 
 
 < 
 1— 
 
 CO 
 CJ 
 
 u_ 
 
 Ll. 
 
 < 
 
 ex 
 
 1— 
 
 2 
 
 c_> 
 
 ex 
 
 l— 
 
 O 
 
 2 
 
 s: 
 
 ex 
 
 CX 
 
 CJ 
 
 1— 
 
 
 CO 
 
 
 
 
 
 
 
 
 
 
 CJ 
 
 to 
 
 ex 
 o 
 
 |NJ 
 
 UJ 
 
 CX 
 
 X 
 CJ 
 
 LX 
 
 o 
 
 CD 
 
 CD 
 
 O 
 
 UJ 
 CX 
 
 CO 
 
 =5 <C 
 
 o- f— 
 
 CO CO 
 
 "-< o 
 
 ex cj 
 
 to 
 
 LxJ 
 CX 
 
 22 
 
Table 2.4-6 NYSIIS Name Coding Rules 
 
 1. If the first letters of the name are 
 'MAC then change these letters to 'MCC 
 ' KN' then change these letters to ! NN' 
 'K' then change this letter to 'C 
 
 ' PH* then change these letters to 'FF' 
 
 1 PF ' then change these letters to 'FF' 
 
 'SOT then change these letters to 'SSS' 
 
 2. If the last letters of the name are 
 'EE' then change these letters to 'Y0' 
 'IE' then change these letters to 'YJ6' 
 'DT 1 or 'RT' or 'RD' or 'NT' or 'ND' 
 
 then change these letters to ' DJ6 " 
 
 3. The first character of the NYSIIS code is the first character of 
 the name. 
 
 4. In the following rules, a scan is performed on the characters of 
 the name. This is described in terms of a program loop. A pointer 
 is used to point to the current position under consideration in the 
 name. Step 4 is to set this pointer to point to the second charac- 
 ter of the name. 
 
 5. For each successive position of the pointer, only one of the follow- 
 ing statements can be executed. 
 
 a. If blank go to rule 7. 
 
 if the current position is a vowel (AEIOU) 
 
 and if it is E followed by V then change to ' AF' 
 
 otherwise change current position to 'A 
 
 i 
 
 b. If the current position is the letter 
 'Q' then change the letter to 'G' 
 
 1 Z 1 then change the letter to 'S' 
 'M' then change the letter to 'N' 
 
 c. If the current position is the letter ' K' 
 and if the next letter is ' N' then replace 
 the current position by 'N' otherwise replace 
 the current position by ' C 
 
 d. If the current position points to the letter string 
 'SCH' then replace the string with 'SSS' 
 
 'PH' then replace the string with 'FF' 
 
 e. If the current position is the letter 'H' and either 
 the preceding or following letter is not a vowel (AEIOU) 
 then replace the current position with the preceding letter. 
 
 23 ——————— 
 
Table 2.4-6 NYSIIS Name Coding Rules (Continued) 
 
 f. If the current position is the letter 'W and the preceding 
 letter is a vowel then replace the current position with the 
 preceding position. 
 
 If none of these rules applies, then retain the current posi- 
 tion letter value. 
 
 6. If the current position letter is equal to the last letter placed 
 in the code then set the pointer to point to the next letter and go 
 to Step 5. 
 
 The next character of the NYSIIS code is the current position 
 letter. 
 
 Increment the pointer to point at the next letter. 
 Go to Step 5. 
 
 7. If the last character of the NYSIIS code is the letter 'S' then 
 remove it. 
 
 8. If the last two characters of the NYSIIS code are the letters 'AY 1 
 then replace them with the single character 'Y'. 
 
 9- If the last character of the NYSIIS code is the letter 'A' then 
 remove this letter. 
 
 The NYSSIS coding rules are, in several instances, subject to different 
 interpretations. As a result, implementation in software may differ 
 slightly from one system to another. This illustration demonstrates the 
 results of one such implementation. 
 
 24 
 
An example of a pragmatically developed name lookup technique is that in 
 use at the Medical Information Bureau. In this system, neither a 
 Soundex nor a phonetic technique is used. Robert W. Keighley, Director 
 of Systems and Planning at the Recording and Statistical Division of 
 Sperry Rand, the servicing agent for the MIB, describes the technique 
 as follows: 
 
 "The MIB system is a completely automated on-line batch 
 oriented name look-up system in contrast to a visual 
 terminal inquiry response system. The processing of in- 
 quiries is not accomplished through use of an algorithm 
 such as a Soundex or phonetic technique; instead, a name 
 key is generated (group spelling number) through use of 
 our name dictionaries. These dictionaries of surnames and 
 given names reside on a disc file. The dictionary is used 
 only to convert the incoming surname or given name to a 
 unique group number. This group number represents names 
 which are confused with one another. In addition, for 
 given names, alternate groups are generated for situations 
 such as nicknames and other confusions which do not neces- 
 sarily sound like the given name in the inquiry. 
 
 In contrast to other popular algorithms which attempt to 
 accomplish the above, but do not fully, we have manually 
 grouped our names according to our experience over the 
 last eight years. This has come about by having to either 
 search or update at least 160 million records over this 
 span of time. Needless to say, our dictionaries are 
 fairly complete at this point in time. "20 
 
 This technique, used in conjunction with a highly specialized search 
 strategy (discussed later in this section), has given excellent results. 
 Mr. Keighley states that: 
 
 "In order to give you some measure of our success, we 
 handle all the situations above (compound names, nick- 
 names, cross reference names such as aliases and maiden 
 names, etc.) against a file of 14 million records, pro- 
 cessing an average of 80,000 inquiries a day. It should, 
 also, be pointed out that no manual intervention, either 
 at our computer site or at our subscribers location is 
 needed prior to conveying the results of our search to 
 the end user, since we identify the individual with a 
 high degree of accuracy. "21 
 
 20 
 
 Personal Communication from Robert Keighley, Director of Systems and 
 
 Planning, Recording and Statistical Division of Sperry Rand, the servic- 
 ing agent for the MIB, October 14, 1975. 
 
 21 ibid. 
 
 25 
 
This example illustrates the feasibility of developing a highly success- 
 ful retrieval system based on a name lookup technique. But the high de- 
 gree of special treatment given to development of this system was a time- 
 consuming and very expensive process. It is equally important to point 
 out that successful systems based on modified Soundex rules are also 
 available: for example, the retrieval system in use at the Department of 
 Justice in the Immigration and Naturalization Service. In other words, 
 although the MIB system represents a high level of attainment, there 
 exist algorithms within the public domain which can be used very effect- 
 ively. 
 
 A fundamental consideration associated with the adoption of any technique 
 is of course the tradeoff between computer costs for implementing and 
 executing complex algorithms, human resources for monitoring computer 
 activities, and the cost of missing a record in a search, which may have 
 social and legal implications. In view of the latter, it seems more rea- 
 sonable to accept larger selectivity ratios based on name lookup, assum- 
 ing that other non-unique attributes caji be effectively used to filter 
 lists of file items retrieved by surname lookup. 
 
 2.5 IMPLEMENTATION OF NAME LOOKUP TECHNIQUES 
 
 The high selectivity of value of name as a non-unique identifier has 
 prompted the emphasis on name lookup techniques. But it is also true 
 that this emphasis has repercussions regarding selection of retrieval 
 techniques. Some of the more sophisticated techniques will not be 
 easily available due to the nature of possible errors in the data. For 
 example, it is doubtful that hashing techniques would be useful in the 
 processing of surnames since a misspelling either at data entry or re- 
 trieval request time coul-d result in a complete misdirection of the 
 search. 
 
 Use of name lookup techniques facilitates grouping of closely related 
 names. Thus, if SMITH and SMYTHE are encoded in such a way that a sort 
 of the file on the encoded field places them close to each other, the 
 likelihood of finding the alternate spelling during a search is greatly 
 enhanced. For example, if the Soundex code appears to be adequate for 
 handling the mix of names in the file, the approach taken could be as 
 follows: apply the Soundex algorithm to the SURNAME field in all records, 
 perform a sort on the encoded field, and write the records, in sort order, 
 on a storage device. The records will be recorded in convenient logical 
 groups variously called blocks or buckets . (Both terms imply the number 
 of physical records involved in a single read or write operation.) 
 
 If the procedure described above were followed, it would be likely that 
 SMITH and SMYTHE would be recorded in the same, or at least in adjacent, 
 buckets. Indices would then be formed for use in locating buckets during 
 a search. In a small file, there might be a single level of index based, 
 for example, on the Soundex formulation of the input surname. The first 
 two or three alphanumerics of the resulting code could point to a bucket 
 
 26 
 
that would be expected to contain the desired record. In a larger 
 system, a multi-level index scheme might be used with the highest level 
 pointing to a secondary index containing a breakdown of names starting 
 with the letter 'S.' In all cases, the indicated bucket will be read 
 in and a matching algorithm followed until either an exact match is 
 found or the contents of the bucket have been exhausted. If no match 
 is found, the search may continue through use of the preceding and sub- 
 sequent buckets. In some cases, it may be reasonable to look in logical 
 blocks that are two or three buckets away since in the case of very 
 common names the instances may stretch over several buckets. 
 
 The system developed by the Immigration and Naturalization Service fol- 
 lows this type of approach with the Soundex codes producing the top 
 level index. The resulting groupings are then further segregated on 
 the basis of the first names. The third level of index, BIRTHDATE, 
 gives flexibility to the retrieval system. The program is capable of 
 handling requests for the records of individuals of exactly this age or 
 for the records of those within bounds of plus or minus nine years. The 
 user has the option of asking for a count of hits before results are 
 printed in case an unmanageable number of records have been retrieved 
 due to specification of a wide age range. 22a Obviously this option is for 
 use by inquirers who are not sure of the individual's exact age and not 
 by the individual requesting his own record. However, the number of 
 problems than can occur with the AGE/BIRTHDATE variable make it not un- 
 likely that an individual could be mistaken about how his own age has 
 been recorded. The handling of such an error could be simplified by 
 the availability of a range field. 
 
 The search mechanism employed by the Immigration and Naturalization 
 Service necessitates use of three levels of indices before the actual 
 accessing of the record (s). The number of required index or file 
 searches can be reduced through use of encoding techniques to form a 
 compound pointer. A clever approach to using several attributes as a 
 record-key index or compound pointer was developed for the Central 
 California Red Cross Blood Center. The program, whose main purpose is 
 to maintain and develop a donor pool of repeat donors, was in operation 
 at Stanford University at the time of this study. 
 
 "Donor records are... stored in contiguous blocks by blood 
 type, 30/block, and the donor indexes that point to these 
 records are stored 255/block. By this method of indexing, 
 the indexes themselves can be sorted by blood type, date 
 of last donation, etc. relatively easily so that one set 
 of donors can be dealt with individually. "22b 
 
 ZZapersonal Communication from Robert Robinson, Director of ADP Systems 
 Branch, Immigration and Naturalization Service, Department of Justice, 
 September 22, 1975. 
 
 22b|_udwig, H. R., "An Interactive Computer-Based Donor Management System," 
 Computers in Biology and Medicine , 5 (1975), pp. 69-75. 
 
 27 
 
The donor index contains the following attributes: 
 
 - a two-byte donor i.d. consisting of the first three let- 
 ter's of the last name and the birthdate. This informa- 
 tion is reduced to two bytes through use of the following 
 algorithm: 
 
 3 
 
 Key = E (a?26 3-1 )+J-2400 
 i=l ] 
 
 where a- denotes the numerical value of each of the first 
 three letters of the last name, e.g. A = 1,B = 2, ...and 
 J denotes the Julian value of the birthdate with the base 
 
 be inn 1900. 
 
 - a two-byte pointer that specifies both the physical block 
 and the logical record number. For the current file, 
 this is a number between 1 and 29,940. The organization 
 of the file is such that this number also implies blood 
 type. 
 
 - a two-byte data field to indicate date of last donation. 
 To reduce field size, this is a Julian date from some 
 base year (in this case, 1972). 
 
 - two one-byte fields for coded information: number of 
 entries on the eligibility list, total donations to 
 date, indication of rare blood types, etc. 
 
 The. system has been designed so that updating of some levels of infor- 
 mation can be handled by reading in only the index level, and, when a 
 donor record is required, it can be fetched directly. 
 
 A yery sophisticated example of use of an encoding technique occurs in 
 the MIB system. This system places a high premium on accuracy of 
 retrieval. Dictionary lookups are performed to obtain group numbers 
 both for surname and given name. Mr. Keighley has summarized the search 
 algorithm as follows: 
 
 "The process of looking up an individual using surname, given 
 name, date of birth, place of birth (optional) and territory 
 of residence (optional) is as follows: 
 
 1. Look up surname in surname dictionary to obtain its group 
 
 number. 
 
 2. Look up given name in given name dictionary to obtain its 
 group number and any alternatives (nicknames etc.). 
 
 28 
 
3. Generate a key consisting of surname qroun number, given 
 name group number, date of birth (coded), place of birth 
 (optional-coded), and territory (optional-coded). 
 
 4. Using the generated key from Step 3, search the data base 
 on a direct access basis (index sequential) for records 
 applicable to the inquiry. 
 
 5. Screen each record in detail and mathematically determine 
 what the degree of probability is that the record being 
 screened pertains to the person being searched. 
 
 6. If any record being screened has a high enough proba-„ 
 bility, it is selected as a response to the inquiry." 
 
 In this instance the encoding technique has not cut down on the number 
 of required accesses, but has instead been used to generate a very 
 accurate filter. 
 
 2.6 OTHER SEARCH STRATEGIES 
 
 Analysts considering potential system changes will do so in the context 
 of the operational environment of their agencies. In some instances, 
 no great emphasis will be placed on search strategy. Yet, in other 
 situations, particularly when the file size is large, the selection of 
 an efficient search strategy will be crucial to effective system per- 
 formance. The number of requests to be processed, batch/interactive 
 capabilities of the system, size of the file, etc., will be determining 
 factors in these decisions. A general discussion of some available 
 search strategies is given here as background for those who must concern 
 themselves with this issue. 
 
 W.A. Burkhard and R.M. Keller have described an approach to the problem 
 of "...searching the set of keys in a file to find a key which is 
 closest to a given query key... Three file structures are presented to- 
 gether with their corresponding search algorithms, which are intended to 
 reduce the number of comparisons required to achieve the desired re- 
 sult." 24 They describe their result as follows: 
 
 "The study described here is concerned with the general 
 problem of efficiently searching files in the following 
 situations: (1) find a keyinthe file closest to a 
 given query key, and (2) find all kevs in the file 
 
 23 
 
 Keighley, op.cit, 
 
 W. A. Burkhard and R. M. Keller, "Some Approaches to Best-Match File 
 Searching," Communications of the ACM , 16, (4 April 1973), p. 230. 
 
 29 
 
closest to a given query key. By 'closest' we mean ac- 
 cording to some suitable measure of distance, specifically 
 a 'metric' in the mathematical sense. These situations may 
 arise in information retrieval applications in which mem- 
 bers of a file are keyed to a number of numerically-re- 
 presented attributes. Our search algorithms have the 
 following common basis: let b be the 'best' key found at 
 a certain point in the application of the algorithm. 
 Based on certain 'cutoff criteria' which depend on b, sub- 
 sets of tne file remaining, to be examined are eliminated 
 without explicitly comparing the keys in these subsets 
 with the query key. Our experimental results (on randomly- 
 generated files) indicate that large segments of the file 
 may be eliminated from consideration using these cutoff 
 criteria. "25 
 
 Abraham Bookstein has described a hybrid search technique which 
 "...has the advantage of retaining the simplicity of search keys while 
 also including some of the flexibility that Boolean expressions of key 
 words have for uniquely defining an item. ...The only indexes that 
 must be maintained are the hash tables; the other indexes. . .are replaced 
 by the search algorithms. "26 Bookstein goes on to say: 
 
 "A user would begin by entering into the system search key. 
 If the system finds that the number of items that would be 
 retrieved exceeds a preset threshold, it would output a mes- 
 sage requesting that the user enter a set of key words taken 
 from various fields in the records; ...The system first genera- 
 tes a subfile of records having the desired search key. If 
 a hashing technique is used, constructing this subfile can be 
 accomplished quickly and at a relatively little cost in space 
 for tables. Once the smaller file is formed, a complete 
 search of the full records can be made for the key words. 
 Since the system operates in two phases, it is less sensitive 
 to the number of records the search key retrieves as far as 
 user considerations are concerned. Ease of use becomes the 
 dominating objective in designing the search key ... In the hy- 
 brid system, we can think of the search key not as an access 
 mechanism ... but rather as a file reduction mechanism. This 
 system trades the cost of maintaining and storing large indexes 
 for an increase in costs of computer processing; only relatively 
 easily maintained hash tables for fixed length search keys need 
 to be maintained. "27 
 
 25 
 
 Ibid. , p. 236, 
 
 26 Abraham Bookstein, "A Hybrid Access Method for Bibliographic Records, 1 
 Journal of Library Automation , 7:97-104, (2 June 1974), p. 99. 
 
 27 ibid. 
 
 30 
 
The use of Malcolm Harrison's hash technique to expedite the search is 
 explained as follows: 
 
 "A fixed number of bits, or signatures, (is) added to each 
 field on which a search can take place; these additional 
 bits are derived in a well-defined way from the original 
 field. This subfield is a fixed-size representation of the 
 full field in a form that can be used to very rapidly elimi- 
 nate most records which would not pass the key word matching 
 test. It is stored in the index to the file alona with the 
 address of the record. Though' this preliminary test is not 
 foolproof, it could considerably reduce the size of the sub- 
 file that requires a more costly complete search, thereby 
 reducing the number of disc accesses. "2° 
 
 The author quotes a false drop rate of approximately ten percent for the 
 bibliographic application being described. This is of course too high 
 a rate for personal data file retrieval systems, yet a careful implementa- 
 tion of a similar combination of such techniques might be appropriately 
 applied to the retrieval problem at hand. 
 
 Retrieval techniques described in this section were selected because of 
 their relevance to the present discussion of use of non-unique identifiers 
 in search algorithms. Those interested in pursuing a broader range of 
 retrieval techniques might begin with Dennis Severance's generalized 
 model for exploring the relationship between alternative search techni- 
 ques^ and Knuth's work on sorting and searching30. 
 
 28 ibid. 
 
 29 
 
 Dennis G. Severance, "Identifier Search Mechanisms: A Survey and 
 
 Generalized Model," Computing Surveys , 6 (3 September 1974), pp. 175-194, 
 
 30 
 
 Knuth, op.cit. 
 
 31 
 
3.0 IDENTIFICATION OF WEIGHTING FACTORS 
 
 3.1 ACCURACY CONSTRAINTS IMPOSED BY THE PRIVACY ACT 
 
 One of the most important facets of the privacy legislation is the 
 imposition of accuracy constraints on the agencies maintaining personal 
 data files. The intent of these provisions is to protect the individual's 
 privacy by insisting that data pertaining to him be correct, up-to-date, 
 and relevant. The new law gives the individual the right to review his 
 own record and to request that any relevant corrections be made. Disputed 
 information maintained in the file must be so identified. 
 
 This emphasis on accuracy of personal data held in computer files is a 
 function of data availability, not of an increase in error rates due to 
 computerization of the data. Manual record-keeping systems have, in 
 general, a higher error rate than computer systems. Susan Wooldridge, 
 et aT. have stated that an error rate of up to 10% is not unusual in 
 paper-based files. "Indeed a frequently used justification for computer- 
 izing is to raise the accuracy of the files. "3* 
 
 Alan Westin has quoted Dr. Collen of Kaiser-Permanente as indicating that 
 manual systems in the hospital setting are plagued by errors. 
 
 "Errors have become so common in traditional medical records 
 that physicians are accustomed to handling them, from finding 
 a woman's lab test in her husband's file, to prescription 
 errors in the hospital. "32 
 
 Dr. Collen went on to say that, after installation of a computerized 
 record-keeping system, they had found that: 
 
 "...some 10 percent of our computer records contain 'errors.' 
 We don't know what the error level in manual files is, but 
 it certainly couldn't be less. Gradually we hope to get 
 the computer file error level down to around three percent. 
 To get to percent would be prohibitively costly, and 
 probably isn't possible, anyway. "33 
 
 Before the emergence of computerized files', few organizations took time 
 to study their own systems from the point of view of accuracy and possible 
 means of improvement. 34 There was far less concern with the problem, to a 
 
 31 
 Wooldridge, Susan, Colin Corder, and Claude Johnson, Securi ty Standards 
 
 for Data Processing , John Wiley and Sons, New York, 1973, p. 60. 
 
 32 
 Westin, A., Databanks in a Free Society , Quadrangle Books, 1972, p. 210. 
 
 33 U)id. 
 
 34 Ibid , p. 433. 
 
 32 
 
great extent because clues other than the data itself were readily avail- 
 able. These clues took the form of the handwriting in which data was 
 recorded, the color of the form used, the placement of the paper in the 
 file, the age of the document, etc. In many cases, the person reviewing 
 the data was conversant with the general structure of the file, and often 
 even with the individual case. He thus brought his own background to the 
 perusal of the material. 
 
 Automation has had the effect of depersonalizing the data, reducing it 
 artificially to a level of uniformity that masks the disparities in 
 accuracy while at the same time making it readily available to a broad 
 spectrum of users. The loss of visual clues such as ancient appearance 
 of the document or illegibility of the data is particularly important 
 from the point of view of timeliness of the data maintained in the file. 
 
 James Martin, in his book entitled Security, Accuracy, and Privacy in 
 Computer Systems , has discussed this problem. 
 
 "In addition to patent errors, large files also accumulate two 
 other kinds of troublesome material --the decaying and the un- 
 eval dated. The former is characteristic of biographical details. 
 Information gets less reliable the farther away it is from the 
 source. References and testimonials, to say nothing of college 
 degrees, lose their relevance as their subject grows older. The 
 facts remain facts in the environment of their collection, but 
 they may be inappropriate in the context of the use to which 
 they are put. "35 
 
 The decay factor inherent in much biographical data might be thought of 
 as the length of time during which the data is more likely to be right 
 than wrong. For some variables, such as birthplace, there is no decay. 
 For others, such as current age, decay is at a constant rate. For still 
 others, the amount of decay will depend upon the time at which the data 
 was collected, e.g., the variable HIGHEST DEGREE ATTAINED will have a 
 much greater potential for change if it refers to a student than if it 
 refers to a professional person. 
 
 Form 3.1-1 identifies some of the standard biographical data items that 
 are subject to this type of variability. The privacy legislation has 
 emphasized the importance of the timeliness factor since clearly much of 
 this data deteriorates over time. In the context of the current discus- 
 sion, this factor is an important indicator in the assignment of weighting 
 scores to be used in a final determination of selection of non-unique 
 identifiers for retrieval algorithms. 
 
 35 
 Martin, J., Security, Accuracy, and Privacy in Computer Systems 
 
 Prentice-Hall, New Jersey, 1973, p. 431. 
 
 33 
 
Form 3.1-1 Decay Factors of Standard Biographical Date Elements 
 
 
 
 DECAY FACTOR 
 Does this variable remain constant 
 over time? 
 
 NON-UNIQUE IDENTIFIER 
 
 YES* 
 
 NO** 
 
 LAST NAME 
 
 MALE 
 
 / 
 
 
 FEMALE 
 
 
 / 
 
 First Name 
 
 / 
 
 
 Middle Name 
 
 / 
 
 
 Sex 
 
 / 
 
 
 Birthdate 
 
 / 
 
 
 Birthplace 
 
 / 
 
 
 Marital Status 
 
 
 / 
 
 Address 
 
 
 / 
 
 Occupation 
 
 
 / 
 
 Education 
 
 
 / 
 
 Physical Characteristics 
 
 / 
 
 
 *Changes are rare 
 **Changes are not unusual 
 
 34 
 
3.2 DETERMINATION OF WEIGHTING FACTORS FOR EXISTING FILES 
 
 In the establishment of a computer file, errors can be introduced at any 
 stage of data handling. They sometimes result from the individual's 
 misunderstanding of a question, sometimes it is a transcription error on 
 the part of the clerk filling out a keypunch form, sometimes it is a 
 problem in the file management system that creates or updates the record. 
 A determination of the likelihood of such errors having occurred requires 
 evaluation of procedures followed during the key stages in data handling: 
 
 1. data base definition and forms design 
 
 2. data collection techniques 
 
 3. keying of data and input controls 
 
 4. computer edit routines 
 
 5. manual accuracy check of record as printed by the computer 
 
 Thus, an acquaintance with the evolutionary process through which the 
 file came into existence is essential to an evaluation of the degree of 
 accuracy of the data it contains. A comprehensive examination of this 
 evolutionary process should answer such questions as: is the data 
 contained in the files consistent? (i.e., did all of the individuals 
 who filled out the forms understand the same thing by the questions? 
 Was the question asked the same way each time or has the wording changed 
 significantly?) and: which data items in the file are likely to be 
 most reliable? 
 
 The following sections describe each stage of data collection in terms of 
 appropriate error detection procedures. Acknowledgement of the possible 
 existence of such errors is important, but, since data base deficiencies 
 will differ greatly from system to system, it would be helpful to be able 
 to characterize these weaknesses in more specific terms. One approach 
 would be to assign weighting scores to the various biographical identifiers 
 in the file in such a way that the weights serve as indicators of the 
 adequacy of the collection procedures for each variable. In the following 
 discussions, a vehicle for making such a determination has been suggested. 
 Included with the description of each stage of data collection is a table 
 in which YES/NO questions are postulated for each of the problem areas 
 identified. These questions are to be asked for each of the biographical 
 identifiers used in the hypothetical file under consideration. A summary 
 chart is used to record the total number of NO checks for each of the data 
 elements. These totals then become the identified weighting scores for 
 the various data items and thus produce a gross picture of the validity of 
 the biographical identifiers in the file. 
 
 The weights thus arrived at for each of the standard biographical data 
 elements will be used in a final determination of identifiers for the 
 retrieval process (see Section 4). 
 
 35 
 
3.2.1 Data Base Definition and Forms Design . The determination of an 
 appropriate set of data items to be included in a system is an important 
 part of the design. If the items requested are inappropriate or the 
 wording of the question obscure, difficulties arise which are not obvious 
 from looking at the data. The importance—and the difficulty— of obtain- 
 ing such uniformity of data is discussed in the Uniform Hospital Discharge 
 Data Demons tration report. This project was directed toward improvement 
 in health care deTi very through collection and evaluation of discharge 
 data from a large number of hospital facilities throughout the country. 
 A subcommittee of the U.S. National Committee on Vital and Health 
 Statistics handled selection of the basic data set. Collection of the 
 data was carried out systematically, with personnel at the hospital sites 
 being trained with respect to abstracting of data items from the charts, 
 and quality control checks implemented throughout the data collection 
 procedure. 
 
 "In all instances, the system manuals used in the test hospitals 
 were modified to conform the UHDDD manual .. .Coding examples were 
 provided to elaborate on the individual items and to cover 
 anticipated contingencies such as when the date of birth, ZIP 
 code, or married status was unknown, when patients from foreign 
 countries who do not have ZIP codes were treated, etc. Examples 
 to clarify the concepts of the principal diagnosis and the 
 principal procedure were also given. "36 
 
 Even so, many data collection problems have occurred. For example, the 
 categories established for MARITAL STATUS are: 
 
 "Now Married refers to persons who state they are married and 
 not separated. Divorced or Separated is used for persons who 
 state they no longer live together, whether or not legal action 
 has been taken. Never Married (Single) refers to persons who 
 state they have never married or whose only marriage has been 
 annulled. Widowed includes widows and widowers. "3/ 
 
 Yet, as noted in Section 2.2, there were difficulties in collecting even 
 this very well -defined data in the following instances: 
 
 "Persons living in common-law relationships who do not wish to 
 be classified under any of the alternatives. Divorced persons 
 who consider themselves single and state so at admission. 
 Single females admitted for childbirth do not generally want 
 to be identified as single and state they are married. "38 
 
 36 
 
 Hodgson, D. A., L. E. Kucken, and J. M. Ensign, The Uniform Hospital 
 Discharge Data Demonstration, Summary Report , Health Services Founda- 
 tion, Chicago, Illinois, 1973, p. 24. 
 
 37 Ibid , p. 12. 
 38 Ibid, p. 31. 
 
 36 
 
They conclude: 
 
 ''The original intent of this item was to attempt to secure some 
 indication of the living arrangement of a patient, but it proves 
 to be of marginal utility for this purpose. This is a complex 
 variable to establish if the many socio-economic factors which 
 enter into the underlying health care implications associated 
 with living arrangements are considered. "39 
 
 Form 3.2.1-1 contains an analysis of the inherent characteristics of a 
 sample set of biographical data elements. The results indicated in the 
 form reflect good experience, but it is assumed that counter instances 
 could be found. The analyst should compare his own evaluation of similar 
 elements contained on the forms used in his data collection procedures 
 with these results. 
 
 The data requested in Form 3.2.1-2, unlike that shown in the earlier 
 forms, is dependent upon the actual collection procedures involved and 
 is, therefore, left empty. The analyst is encouraged to produce similar 
 tables, using the identifiers relevant to his file, and then, using a 
 selection of random records from the data base in conjunction with the 
 forms on which the original data collection took place, to answer the 
 questions in the table. The answers will supply the required weights 
 for this stage of the data collection procedure. 
 
 3.2.2 Data Collection Techniques . Perhaps the best possible guideline 
 for successful data collection would be: capture the data as close to 
 the source as possible. This might equally well be stated as: the fewer 
 manual transcriptions of the data, the cleaner the data. The reasoning 
 behind this is that the individual is normally the person best qualified 
 to monitor collection of information about himself. He would not be as 
 likely, for example, to transpose numbers in his street address as would 
 a clerk whose job is to transfer his descriptive data onto a keypunch 
 form. Assuming this is a reasonable generalization, an ideal data col- 
 lection system would then call for the individual to enter his data 
 directly onto a keypunch form (which need not look like the standard 80 
 column form, but which must be marked with column numbers that are re- 
 cognizable to the keypunch staff), or, in some cases, for the individual 
 to interact with the computer, via terminal, in a question/answer dia- 
 logue. If the questions are easily understood, if they are available in 
 the language spoken by the individual, and if he prints (or types, if a 
 terminal is used) acceptably well, this may indeed be the best way to 
 capture the data. 
 
 39 
 ""ibid. 
 
 37 
 

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 38 
 
refully 
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 & place names (Des 
 Moines)?* . 
 
 I 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 structions 
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 with examples 
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 39 
 
The problem is usually not so straightforward, however. If any ambiguity 
 exists in the wording of the question, or if coding of the data is re- 
 quired, it may be far more relevant to have the forms filled out by 
 trained interviewers who understand the intent of the questions and can 
 guide the individual in determining what is for him, the appropriate 
 answer. 
 
 These generalities concern data collected specifically for entry into 
 the computer. There is another set of instances which involve collec- 
 tion of data after the fact, as in the case in the Uniform Hospital 
 Discharge Data Demonstration summary . Here existing medical records are 
 being assigned to the abstracting task. The methodology for this 
 abstracting procedure has been carefully laid out. Even so, the accuracy 
 checks carried out by means of a re-abstracting procedure for the data 
 collected in Months 1 and 4 at each hospital in some cases reflect a 
 surprisingly high error rate. 
 
 In the following paragraphs, the re-abstracting procedure is quoted as 
 it appears in the project manual, along with the summary rates of errors 
 that occurred. 
 
 "The demonstration design called for two samples of medical 
 records to be re-abstracted in each test site. The first re- 
 abstracting was designed to compare the data produced by 
 applying the UHDDD definitions to the data generated from 
 the same medical records using the definitions in effect 
 prior to the demonstration. The second re-abstracting com- 
 pared the data produced by the hospital during the fourth 
 month of the demonstration, with the data re-abstracted by 
 the project medical record librarians. . .Table 2 provides a 
 summary of the discrepancy rates for individual items from 
 the second re-abstracting analyses. In almost all instances, 
 the errors with items having low discrepancy rates were the 
 result of clerical errors; e.g., an error in transcribing 
 data from the medical record to the abstract. 
 
 40 
 
ITEM 
 
 Table 2 Percentage of Abstracts with 
 Discrepancies by Test Sites 
 
 MAINE PENN. WIS, 
 
 CALIF, 
 
 PERSON I.D. 
 
 
 0.1 
 
 0.6 
 
 NA* 
 
 0.1 
 
 DATE OF BIRTH 
 
 
 0.3 
 
 1.2 
 
 — 
 
 0.7 
 
 SEX 
 
 
 0.8 
 
 0.6 
 
 1.2 
 
 0.5 
 
 MARITAL STATUS 
 
 
 1.0 
 
 0.8 
 
 1.8 
 
 1.1 
 
 RACE 
 
 
 0.2 
 
 0.4 
 
 1.0 
 
 0.5 
 
 ZIP CODE 
 
 
 
 1.1 
 
 2.6 
 
 0.5 
 
 ADMISSION DATE 
 
 
 0.1 
 
 — 
 
 0.3 
 
 — 
 
 ADMISSION HOUR 
 
 
 1.3 
 
 24.0 
 
 15.5 
 
 2.0 
 
 DISCHARGE DATE 
 
 
 0.1 
 
 0.2 
 
 NA 
 
 — 
 
 ATTENDING PHYSICIAN 
 
 
 0.4 
 
 1.4 
 
 11.6 
 
 4.1 
 
 OPERATING PHYSICIAN 
 
 
 0.1 
 
 2.4 
 
 19.7 
 
 NA 
 
 PRINCIPAL DIAGNOSES 
 
 
 8.3 
 
 11.8 
 
 10.7 
 
 NA 
 
 OTHER DIAGNOSIS 
 
 
 14.6 
 
 21.0 
 
 30.5 
 
 NA 
 
 PRINCIPAL PROCEDURE 
 
 
 1.9 
 
 6.5 
 
 22.4 
 
 NA 
 
 OTHER PROCEDURES 
 
 
 1.5 
 
 9.3 
 
 15.8 
 
 NA 
 
 DATES OF PRINCIPAL AND 
 
 
 
 
 
 OTHER PROCEDURES 
 
 
 0.1 
 
 7.4 
 
 18.2 
 
 — 
 
 SERVICE TO WHICH ADMITTED 
 
 2.6 
 
 3.2 
 
 3.7 
 
 2.5 
 
 DISPOSITION OF PATIENT 
 
 0.4 
 
 1.2 
 
 3.9 
 
 0.4 
 
 PRINCIPAL SOURCE OF 
 
 PAYMENT 
 
 3.7 
 
 2.2 
 
 1.6 
 
 2 . 7 40 
 
 *Not Available 
 
 Obviously the principal difficulty in this data collection procedure is 
 in the training of clerical staff charged with properly interpreting the 
 available data. C. R. Newing, in discussing data collection problems in 
 the insurance business, points up the necessity of checking data collec- 
 tion procedures and tracing errors back to the point of origin. 
 
 "In our efforts to control quality, training of clerical staff 
 plays a large part particularly as most of the data sheets 
 which we use are quite complicated and contain a large amount 
 of information in coded form. We have found it worth while to 
 return documents which contain errors to the originator and 
 while this is not popular it does seem to be having effect. 
 There was a time when 20 percent or so of documents contained 
 errors but we have reduced this to around 5 percent... Having 
 adopted the principle that the files which we hold are the 
 responsibility of the user departments as far as content and 
 accuracy are concerned, we co-operate in helping them to apply 
 any checks which they may wish to operate. "41 
 
 40 
 
 41 
 
 Ibid -, p. 38. 
 
 Newing, C. R. , "Data Arrangement and Checking in the Insurance 
 Business," Applied Statistics , Vol. 2, No. 1, 1972, pp. 45-52. 
 
 41 
 
In another paper on a similar subject, W. F. Kemsley emphasized the 
 detailed analysis required for data that is initially "crude" in form. 
 He reported on a survey directed at collection of "...statistical data 
 on monetary transactions in the personal section." Data collection 
 procedures called for family interviews as well as preparation of a 14- 
 day diary by the family. Many problems were encountered by the editors. 
 
 "Some deficiencies become obvious as soon as the entries are 
 examined; others come to light when data in one part of the 
 budget are compared with another part. Some inconsistencies 
 are found in comparisons between diary entries and interview 
 data; others when interview data are compared with individual 
 or family circumstances. "42 
 
 In general, he felt that the necessity for making editing changes had 
 resulted from: 
 
 "(a) duplicated information; 
 
 (b) difficulties in applying definitions; 
 
 (c) incomplete or inconsistent information; 
 
 (d) timing of transactions; 
 
 (e) changes in household composition."^ 
 
 Form 3.2.2-1 contains a set of questions relevant to the methodology of 
 data collection for each item. If the information contained in the file 
 was handled differently at different stages of evolution, the analyst may 
 want to fill out two sets of tables, or he may choose to use the latest 
 methodology. If he does this, he must remember that his final results 
 do not fully reflect the status of the current file. 
 
 3.2.3 Keying of Data and Input Controls . The entry of data into a com- 
 puter file is accomplished in one of two ways: interactively, via 
 terminal operating in a real time environment, or batch, with data being 
 keyed ahead of time to cards, tape, disk, etc. Error rates for these 
 methods tend to be in the same general range (from 1-4 percent). Con- 
 sequently, it is the degree of control placed on the overall handling 
 of the data by the system that makes the difference, and not the choice 
 of keying device. Entry of data via terminal, however, creates an 
 entirely different set of problems. In this case, control is a function 
 of system efficiency and error rates will depend on the degree of 
 sophistication of the programs themselves. James Martin has discussed 
 this difference in approach: 
 
 42 Kemsley, W. F. , "Pre-Computer Editing of Budgets for the Family Expen- 
 diture Survey," Applied Statistics , Vol. 2, No. 1, 1972, pp. 58-64. 
 
 43 
 HJ Ibid. 
 
 42 
 
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 43 
 
"In some systems all information stored originates from the 
 terminal operators. Sometimes the terminal operators work in 
 a fairly casual manner, compared with the card-punch operators 
 and verifiers of batch data processing. Clearly the viability 
 of the concept of building up a data base in this way depends 
 upon whether we can control the accuracy of the terminal input, 
 catching the errors that occur. 
 
 "On certain systems the errors are cumulative. The files con- 
 tain information about a set of items that is kept for several 
 months and is updated periodically by terminal operator actions. 
 If occasional operator actions cause errors in the files, as 
 the months pass by the files will steadily collect more and 
 more inaccuracies. This situation could clearly bring the 
 system into disrepute, and controls must be devised to prevent 
 it. 
 
 "A number of factors make real time systems worse than batch 
 systems for the control of accuracy. First, there are likely 
 to be more terminal operators creating the input and they are 
 scattered over many areas. They tend to be more diverse and 
 less controllable than the operators in a keypunch room. 
 Second, the verification operation found in a keypunch room 
 is usually not employed. Third, batch totals and other batch 
 controls often cannot be used, as the transactions originate 
 singly, not in batches. Fourth, equipment failures will be 
 experienced. It is often when a terminal, line, or computer 
 fails, or during the recover period, that errors originate. 
 
 "We do, however, have one factor that is strongly in our favor 
 in real time systems. This is that in an appropriately designed 
 diaglogue most of the errors made can be caught in re a l time 
 as the operator makes them. The mistake or discrepancy is then 
 rectified on the spot." 4 ^ 
 
 Because of the differences in approach between batch and interactive 
 systems, the respective weighting factors are being handled separately. 
 
 (See Form 3.2.3-1 and 3.2.3-2). 
 
 3.2.4 Computer Edit Routines . Checks by computer program of value 
 ranges, formats, data consistency, etc., not only have the advantage of 
 allowing for rapid processing of the data, but further act on data that 
 has already been stored in the computer file and is, therefore, past most 
 of the stages at which new errors are introduced. Susan Wooldridge et al, 
 comments on the importance of this process. 
 
 44 
 Martin, James, op.cit. , pp. 70-71 
 
 44 
 
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 46 
 
"The first program in any system should be an edit, or a verifi- 
 cation program. All data for a system should go into one simple 
 edit program, if at all possible... The basic principle of the 
 edit program is that it detects all errors that are logically 
 possible to detect, including - or perhaps especially - rare and 
 unlikely ones, for these are the errors which, if allowed to pass 
 into the system cause the most trouble later on. "45 
 
 Programmed checks will, of course, reflect the characteristics of the 
 data in the file but may include format checking, (is there a number in 
 the numeric field?), range (is the age given as greater than 99?), 
 internal consistency (do the birthdate and current age agree?), reason- 
 ableness (would someone really have 30 children?), etc. In some cases, 
 an error situation is obvious as in the case of alphabetic data occurring 
 in a numeric field. Where a determination of reasonableness of the data 
 is required, the computer program may not be able to make a final 
 determination. In such case the data must be flagged for further action 
 by the data processing department. 
 
 Other important factors in determining the likelihood of errors in the 
 file are the length of time for which the computer editing program has 
 been available as well as consideration of the points in time when 
 improvements have been made in its implementation. If only part of the 
 data has been run against the edit program, a first step in data cleaning 
 may well be to run the entire file through the editing routine to assure 
 that all obvious errors have been caught. 
 
 Form 3.2.4-1 lists some of the principal areas of error detection that 
 can be performed by a computer editing program. The analyst is encour- 
 aged to check the variables in the current file against this list. 
 
 3.2.5 Manual Checks for Accuracy . Valuable as other checks on data 
 validity are, there is no substitute for human judgment in reviewing the 
 records. Final determination of the accuracy of a record demands that 
 the printed version of the data be manually checked against the original 
 document. In describing the creation of new files, Wooldridge, Corder 
 and Johnson state that: 
 
 "...after the data has been transferred to magnetic files the 
 first time, it must be printed out in full. Every item must be 
 checked. This is a time-consuming and tedious job; it is the 
 responsibility of the user department, and plans should be made 
 well in advance for laying on extra staff, authorizing over-time, 
 etc. All discrepancies between the computer files and the 
 manual ones must be checked out. Sometimes the error will be 
 in the old files, in which case it must be corrected from 
 
 45 
 
 Wooldridge, Susan, et al . , op.cit. , p. 58. 
 
 47 
 
Are there checks on 
 internal consistency? 
 (Is reported age 
 consistent with 
 birthdate?) 
 
 o 
 
 21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 CO 
 LU 
 
 5- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Are there 
 reasonable- 
 ness checks? 
 (No married 
 6-yr. olds 
 etc.) 
 
 O 
 
 21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 CO 
 LU 
 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Is missing 
 
 data 
 
 reported? 
 
 o 
 
 21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 LU 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Does it check 
 for errors 
 in field for- 
 mats? (Alpha 
 data in nu- 
 meric field, 
 etc.) 
 
 O 
 
 21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 CO 
 LU 
 
 5- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Has all 
 data in 
 the file 
 been run 
 against 
 this edit 
 routine? 
 
 o 
 
 21 
 
 
 
 
 
 
 
 
 
 
 
 
 
 CO 
 
 LU 
 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Does an 
 
 adequate 
 
 edit 
 
 routine 
 
 exist for 
 
 this 
 
 variable? 
 
 * 
 
 o 
 
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 LU 
 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 LU CC 
 
 ZD LU 
 
 o->— 1 
 
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 _1 
 
 LU 
 _J 
 
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 21 
 
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 21 
 
 o 
 
 l-H 
 
 <c 
 
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 Q 
 LU 
 
 PHYSICAL 
 CHARACTERISTICS 
 
 NON-UNI 
 IDENTIF 
 
 
 LAST 
 NAME 
 
 cu 
 +-> 
 u 
 cu 
 
 03 
 
 CO 
 CU 
 
 -O 
 03 
 •i— 
 
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 03 
 > 
 
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 .E 
 +-> 
 
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 o 
 
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 co 
 
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 cu 
 
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 03 
 
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 i— i 
 
 48 
 
scratch... it is very dangerous to start live processing with 
 unchecked data, and it should never be authorized . Print-out 
 and checking should continue for the first few cycles of the 
 system; undetected program bugs and input errors are not only 
 likely but extremely likely ." 2 ^ 
 
 Robert Goldstein, in his Datamation article entitled "The Cost of Privacy" 
 has suggested that, as standard procedures are adopted for reviewing the 
 contents of files for compliance with the Privacy Act, approximately 10% 
 of the records will either be dropped or rechecked in any given year. 
 Under such a procedure, he estimates that: 
 
 "For a typical system, about 10% of the records would expire 
 each year, and 90% of these would be rechecked rather than 
 dropped. "47 
 
 This emphasizes the importance of such manual checks. Richardson and 
 Cunningham have pointed out that audit trails can be kept with each data 
 item in the file in such a way as to enhance the review of edited data. 
 
 "Reviewing of edited data can benefit considerably from 'data- 
 marking' , i.e., storing a bit-string with every item to 
 indicate whether the item: 
 
 (i) has been queried without correction; 
 
 (ii) is to be accepted regardless; 
 
 (iii) has been corrected manually; 
 
 (iv) is a candidate for automatic correction later; 
 
 (v) has already been automatically corrected. "48 
 
 An evaluation of the current file must include an analysis of the type 
 of manual checking procedures employed to date. Form 3.2.5-1 gives the 
 reader an opportunity to score the variables in his file with respect to 
 these manual checking procedures. 
 
 3.3 SUMMARY OF WEIGHTING FACTORS 
 
 Form 3.3-1 can be used to determine final weighting factors for each of 
 the standard biographical data elements in the table. The check marks 
 appearing in the NO columns of each of the preceding charts should be 
 totalled for each variable and the results entered in this form. These 
 results are used to determine candidate fields where error and omissions 
 are 1 ikely to occur. 
 
 46 Ibid., p. 61 
 
 47 Goldstein, Robert C. , "The Cost of Privacy," Datamation , October 1975 
 p. 65. 
 
 Richardson, M. and A. D. Cunningham," Vetting of Industrial Survey 
 Questionnaires," Applied Statistics , Vol. 2, No. 1, 1972, p. 54. 
 
 49 
 
CO 
 
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 CT) 
 
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 cn 
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 o 
 
 Did the personnel 
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 this work? 
 
 o 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 Are update proced- 
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 printing and verify- 
 ing the updated 
 record? 
 
 o 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 LU 
 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Is the printed output 
 checked against 
 original document? 
 
 o 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 LU 
 
 >- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Is the record printed 
 out after storage in 
 file? 
 
 o 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 PHYSICAL 
 CHARACTERISTICS 
 
 NON-UN 
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 CUMULATIVE 
 
 WEIGHTING 
 
 FACTOR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 MANUAL 
 
 CHECK 
 
 FACTOR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 COMPUTER 
 
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 FACTOR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 INPUT 
 
 PROCEDURE 
 
 FACTOR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 COLLECTION 
 
 PROCEDURE 
 
 FACTOR 
 
 
 
 
 
 
 
 
 
 
 
 
 
 DATA 
 BASE 
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 INHERENT 
 CHARAC- 
 TERISTICS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 DECAY 
 FACTORS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 NON-UNIQUE 
 IDENTIFIER 
 
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 PHYSICAL 
 CHARACTERISTICS 
 
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 NAME 
 
 51 
 
4.0 FILE VALIDATION 
 
 4.1 INTRODUCTION 
 
 Agencies currently maintaining personal data files will be concerned 
 with the development of procedures for complying with the Privacy Act 
 provisions regarding the relevance, timeliness, and accuracy of data in 
 the individual records. The file validation techniques thus established 
 will also have implications with respect to the evaluation of the level 
 of accuracy maintained in the file over time. Statistics reflecting 
 the number and type of problems detected when selected records are pro- 
 cessed should give a clear picture of improvements in data handling. 
 For purposes of the current discussion, performance of file validation 
 is crucial to formulation of a reasonably accurate estimate of the re- 
 trieval reliability for each of the variables maintained in the records. 
 
 4.2 VALIDATION TECHNIQUES 
 
 Section 3 emphasized the importance of the source document with respect 
 to the ultimate accuracy level of the file. The CUMULATIVE WEIGHTING 
 FACTOR column in Table 3.3-1, Cumulative Weight Factors for Standard 
 Biographical Data Elements, gives the total number of negative points 
 accumulated for each variable during that analysis procedure. If all 
 of those variables have approximately the same final scores, then the 
 information gained is not discriminatory, but rather implies what is 
 to be anticipated in the area of accuracy for the entire file. If, 
 however, a few of the variables have much higher scores than the others, 
 these should automatically be eliminated from consideration as retrieval 
 keys. These values are not as directly quantifiable as error and 
 omission rates, but should instead serve as guidelines in the retrieval 
 key selection process and, incidentally, as indicators regarding future 
 use of these variables. 
 
 Evaluation of error and omission rates must be accomplished through man- 
 ual checks of the source documents against listings of the records con- 
 tained in the computer file. Trie first decision to be made regarding 
 this procedure involves determination of the number of documents to be 
 checked. A reasonable starting point would be to consider using a small 
 test sample, say 100 records, for this procedure, and then to select a 
 sample size according to the procedure given in Section 5.4. It appears 
 that a determination of error/omission rates would be satisfactory if 
 the standard deviation is less than or equal to .01. The procedure 
 described in Section 5.4 is based on this degree of accuracy. 
 
 Once the sample size has been established, the selection of the actual 
 records can be made. The most important factor here is that the records 
 must be selected from different physical locations within the data base. 
 This is essential to avoid skewing of the results due to system problems 
 such as the existence of bad tape, overwritten disk blocks, etc. Further, 
 since both the accuracy and the nature of the data may have varied over 
 
 52 
 
time, randomness in selection of records is essential to a fair evalua- 
 tion of the state of the data base. The number of physical locations 
 used in a given data base will depend upon its size, but that number 
 should not be less than four if a reasonable sampling is to be achieved. 
 
 For purposes of recording error and omission counts, a chart similar to 
 that shown in Table 4.2-1 should be created. This chart may also serve 
 as an audit document demonstrating the problems detected, resulting error 
 analysis, problems remaining unresolved, action to be taken, and, finally, 
 indication that the file has been corrected and checked. The analysis 
 results for each record should be kept on a separate sheet for ease of 
 use and also as a precaution against the introduction of confusion be- 
 tween records. 
 
 4.3 FILE VALIDATION EXAMPLE 
 
 An example of the techniques involved in manual evaluation of the ac- 
 curacy of the information contained in the files is given in this sec- 
 tion. This discussion is based on use of a small dummy data file con- 
 sisting of one hundred records constructed through random accessing of 
 the telephone book and the random number generator. Separate accesses 
 were made for each component of each name and for each number group. 
 Thus the entries are entirely fictitious . 
 
 The ten records (in this case ten percent of the file) selected for 
 validation are shown, in the most accurate source format available, in 
 Table 4.3-1. This data is assumed to exist on the application forms 
 filled out by each of the individuals. It is also assumed for purposes 
 of illustration that the printouts shown in Table 4.3-2 were obtained 
 for each of these records from the computer file. 
 
 Table 4.3-3 contains the results of the manual check performed on records 
 number 20 and 30. In this case, data has been stored in the file for a 
 set of twins, HECTOR and HARRY GENERAUX. As is clear from the source 
 documents, the twins lived at the same address at the time the applica- 
 tions were filled out. In the intervening period, HECTOR moved to Laurel 
 Street, and a system update was initiated by the operator when he re- 
 ported his new address. The update to the record was made on the basis 
 of SURNAME, and BIRTHDATE, and thus it was easy for the mistake to occur. 
 
 In record number 70 an obvious case of number transposition has occurred 
 in the address field, in this case affecting both street and house 
 number. Record 80 has a mistake in the spelling of the SURNAME where 
 an ' I ' has incorrectly been input as '£'. And in record 100, a mistake 
 has occurred in entering the birthdate. These errors are summarized in 
 Table 4.3-4. 
 
 Use of these rates is discussed in Section 5. 
 
 53 
 

 
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5.0 COMPUTER SIMULATION OF DATA BASE RETRIEVAL 
 
 5.1 PRECISION AND RECALL 
 
 The preceding sections have introduced methodologies for determining the 
 reliability of currently existing data bases. It was seen that a large 
 part of the privacy problem reduces to the technological capabilities of 
 information storage and retrieval systems. This section therefore deals 
 with concepts related to these questions, such as effectiveness of re- 
 trieval techniques, precision, and recall. Finally, the quantification 
 of these concepts provides tools for information systems managers to use 
 in evaluation of the effectiveness of their systems. Computer simula- 
 tions are a powerful aid in performance of such evaluations. Accordingly, 
 a mathematical model and its associated computer simulation are presented 
 in this section. 
 
 As noted in Section 2.4, the effectiveness of information storage and re- 
 trieval systems has classically been measured by two parameters: recall 
 and precision. Recall is defined as the percentage of relevant records 
 which are retrieved in response to a particular query, while precision 
 is the percentage of the retrieved records which are relevant. An ideal 
 system returns 100% of the relevant records in the data base in response 
 to each query, and, in that subset of the data base retrieved by the system, 
 100% of the records are relevant. Such high levels of recall and precision 
 can be simultaneously achieved under the following conditions: 
 
 1. Queries refer only to key fields where the data is perfectly 
 recorded (no errors or omissions). 
 
 2. All of the records in the data base are examined to determine 
 if they meet the query criteria. 
 
 Many storage and retrieval systems are operated as if these conditions were 
 true when, in fact, they are known to be false. Condition 1, perfectly 
 valid data, can be approached, but never really reached; condition 2, 
 examination of all records in the data base, can be simulated by the use 
 of indexed file structures and multi-level searches. Indeed, when 
 queries consist of only terms upon which indexes have been built, condi- 
 tion 2 holds for real data bases. 
 
 It should be pointed out, however, that the recall and precision figures 
 discussed in the preceding paragraph refer only to the relationship be- 
 tween the query as stated to the system and the retrieved records which 
 resulted from the query. Normally this process is simply a surrogate 
 for the more complicated process of locating the record or records in a 
 data base which match some aspect of the real world. Often the criteria 
 by which the user wishes to select a record are not those criteria which 
 were embodied in the indexing system of the data base, thus causing the 
 user's query to be translated into one or more system-oriented queries 
 before the search is carried out. Under these conditions, the most 
 interesting queries are likely to be those which the system cannot answer, 
 
 59 
 
and which are thus not translatable into equivalent system oriented query 
 formulations. Consequently, the system-oriented translations of such 
 queries, even if answered with 100% recall and precision, do not provide 
 high levels of recall and precision with respect to the real query -- 
 that specified by the user. 
 
 Errors in the data base become most important when queries are made very 
 specific and when total recall (with respect to the real world, not just 
 to the query) is required. When, for example, there is a statutory re- 
 quirement to identify the records in a data base which contains personal 
 data in order to provide benefits to those persons who meet the necessary 
 qualifications, there is at least a social cost associated with neglect- 
 ing to properly identify those individuals who should be provided with 
 the benefits, and there is an economic cost which results from improperly 
 providing benefits to ineligible recipients. There is also a statutory 
 requirement that agencies which maintain personal data bases provide, 
 upon demand, the records which pertain to an individual and allow the 
 subject of such records to challenge errors of fact, etc. The problem 
 here is one of providing the inquirer with his, and only his, record, 
 without requiring him to provide sufficient information to create a 
 record on him if it does not already exist. 
 
 Errors in the data base may be of two types: destructive, where the er- 
 roneous data in a particular field errs in such a way that it will not 
 satisfy any real query against the data base, and transformational, where 
 the content of a data field is wrong, but satisfiable. An example of the 
 first type of error would be something like AGE=2B (alpha data in a numeric 
 field), which will never match a numeric query statement. An example of a 
 transformational error is NAME=SMITH (when the name is really SMYTHE). 
 Destructive errors cause a record to become inaccessible via the particular 
 key field in which the key value has been destroyed, while transformational 
 errors will cause the record to be returned in response to queries for 
 which it is irrelevant, and to be missed when it should be found. The 
 effect of error rates in the various data fields employed in a query can 
 be quantified as follows: 
 
 1. Given a query Q consisting of the conjunction of n keys 
 K, , K~, IC, ... K , where the K. each represent a parti- 
 cular value of a particular key field in the data base 
 records, and 
 
 2. If each of thefieldsin the query Q is associated with 
 confidence factors (equal to 1 minus the sum of error 
 and omission rates) c, , c ? , ... c , 
 
 60 
 
then the recall ratio associated with the response to query Q is the pro- 
 duct of the confidence factors associated with each of the key fields:* 
 
 n 
 3- G = TT c. 
 i=l 
 
 It follows that queries which reference more key fields in an attempt to 
 achieve more precision tend to be subject to smaller recall ratios since 
 the c. are always less than or equal to 1. 
 
 5.2 QUERY FORMULATION FOR BEST RESULTS 
 
 Queries should be formulated using the most restrictive key fields in 
 order to provide the most specific answers. Additional precision can be 
 achieved by utilizing the conjunction of more fields in each query, al- 
 though this may result in a decline in the recall ratio, thus causing 
 records to be missed which are actually contained in the data base. Equal- 
 ly, those fields with the lowest error rates (i.e., with the highest con- 
 fidence factors) should be preferred over those known to be badly in error. 
 Qualitatively, the following order provides the greatest discrimination 
 among records: 
 
 1. Name (Surname and Given Name), although the discriminatory 
 power of this field varies depending on the nature of the 
 surname. Matching a name like TREFFTZS provides much more 
 information than matching a name like SMITH, for example. 
 
 2. Surname. 
 
 3. Residence address (Street Number, Street Name, City, and 
 State), although this information tends to decay rapidly 
 with time. 
 
 *Recall ratio is defined as the proportion of relevant records retrieved 
 in response to a query, or equivalently, as the probability of a relevant 
 record being retrieved. For the purpose of this discussion (as pointed 
 out above), a relevant record is one corresponding to an individual who 
 has exactly those attributes defined by the query. However, the actual 
 recorded key values may either be in error or omitted. Thus, the pro- 
 bability of the key value in the ith key field being correct, and hence 
 retrievable by the query, is c-j . If the retrieval mechanism is such 
 that a record is retrieved only if it matches on each of n key values 
 in a query, then it follows that the probability of a relevant record 
 being retrieved is the product of all the n probabilities; i.e., 
 
 G = c r x c 2 x ... x c n = f[ 
 
 i = l 
 
 and so this is the recall ratio. 
 
 c i 
 
 61 
 
4. Date of Birth (Year, Month, and Day). 
 
 5. Place of Birth (City, State), although this also varies: 
 Sparta, Illinois as a birthplace provides more identify- 
 ing information than does New York, New York. 
 
 6. Sex. This is an example of a key field which provides 
 very little information — it may partition the file into 
 two equal halves at best. In more extreme cases where 
 the contents of the file are heavily skewed (all males 
 
 or all females) this field provides, in general, even 
 less information, only being useful in the rarer cases. 
 
 Subjective estimates on the discrimination of various keys are provided 
 in Table 5.3-1 while statistical data on surname records are given in 
 Table 5.3-2, 
 
 5.3 SOFT MATCH TECHNIQUES 
 
 "Soft-Match" techniques may be utilized to overcome the decline in recall 
 which is normally associated with improvements in precision. The increas- 
 ed error rate which is associated with increases in the number of variables 
 entering into the query can be reduced by examining the records retrieved 
 by the sub-queries. For example, a query containing three key fields would 
 ideally return a record (or set of records) in which the three required 
 values are found. Errors in the recording of either the original data 
 or the query may, however, have produced only a partial match, i.e., for 
 either one or two keys. The problem is to devise a method for accounting 
 for these partial matches. Such methods are called "Soft-Match" techniques. 
 
 The result of a Soft-Match Technique is not only a set of retrieval records, 
 but also a numerical weight assigned to each record to indicate its degree 
 of responsiveness to the query. In Appendix III a specific weighting 
 scheme for a Soft-Match Technique is presented. 
 
 5.4 EXAMPLE OF USE OF FILE VALIDATION SUMMARY DATA 
 
 In Section 4.3, an example of the use of file validation techniques is 
 given. Tables 4.3-4 detailed the summary of error and omissions within 
 the sample data file. The final tally of errors shown in the table for 
 each of the variables is: 
 
 Variable Error Count Per 10 
 
 SURNAME 1 
 
 FIRST NAME 
 
 STREET NUMBER 3 
 
 STREET NAME 3 ' 
 
 DATE OF BIRTH 1 
 
 62 
 
Table 5.3-1 Retrieval Ratios (Estimates of Proportion of File Retrieved)* 
 
 Field 
 
 Number 
 
 Contents 
 
 Ret 
 
 rieval Ratio 
 
 1 
 
 
 Day and Month of Birth 
 
 
 1/365 
 
 2 
 
 
 Month of Birth 
 
 
 1/12 
 
 3 
 
 
 Year of Birth 
 
 
 1/50 
 
 4 
 
 
 State 
 
 
 1/50 
 
 5 
 
 
 Sex 
 
 
 1/2 
 
 6 
 
 
 Street Number 
 
 
 1/10,000 
 
 7 
 
 
 Street Name 
 
 
 1/5,000 
 
 8 
 
 
 Education 
 
 
 1/15 
 
 9 
 
 
 County 
 
 
 1/3,000 
 
 10 
 
 
 Date of Birth (YMD) 
 
 
 1/18,000 
 
 *The values used are based on subjective estimates taking into consider- 
 ation the meaning of the keys. These values are used in the model de- 
 scribed in Section 5.5. 
 
 63 
 
Table 5.3-2 Statistical Classification of Surname Records as compiled 
 from Social Security Administration Accounts. 49 
 
 DECILE* 
 1 
 
 Number of Records Retrieved** % of File Retrieved** 
 
 16.35 .00000987% 
 
 2 
 
 408.17 .0002459% 
 
 3 
 
 1,599.28 .0009635% 
 
 4 
 
 4,515.456 .00272037% 
 
 5 
 
 8,000.00 .00481966% 
 
 6 
 
 10,999.94 .006627% 
 
 7 
 
 20,997.32 .01265% 
 
 8 
 
 80,000.62 .048197% 
 
 9 
 
 130,000.80 .07832% 
 
 10 
 
 561,773.3 .3384% 
 
 *Decile 1 corresponds to the least common names, Decile 10, the 
 most common (e.g., Smith). 
 
 **Based < 
 of the 
 
 Dn 1 value per surname key. Each decile contains 10% 
 population. 
 
 49 
 
 "Report of Distributions of Surnames in the Social Account Number 
 
 File," Department of Health, Education and Welfare, June 1, 1964. 
 
 64 
 
These error rates imply that the file as a whole is heavily riddled with 
 serious errors: the single error in the surname sample implies that 10 
 of the records in the file are coded with an erroneous SURNAME -- this 
 is 10% of the file. Three detected errors in the STREET NUMBER again 
 imply that 30% of the street numbers in the file as a whole are in error, 
 etc. Based on this information, a typical query against this file, using 
 the keys of SURNAME, GIVEN NAME, STREET NUMBER, STREET NAME, and DATE OF 
 BIRTH, would have a combined confidence/recall factors of only 39.69% -- 
 less than half of the truly relevant records would ever be located in 
 response to a query. 
 
 Normally, of course, statistical methods would not be used on a file as 
 small as the dummy data base discussed here, but would be used for a file 
 containing thousands of records. The procedure is as follows: 
 
 Suppose we want to determine the error rate (or the omission rate -- the 
 theory is the same) so that the true value differs from the measured 
 value by less than 0.01 with a high probability (say 66%). This means 
 we want to choose a sample size so that the standard deviation in the 
 determination of the error rate is less than 0.01. The standard deviation 
 is given by the formula: 50 
 
 4. 
 
 error rate 
 
 error rate x (1 - error rate) 
 Sample Size 
 
 Therefore, if we wish the standard deviation to be less than 0.01, we 
 must have 
 
 Sample size > 10,000 x error rate x (1 - error rate). 
 
 Since the product of the error rate and its complement is bounded by 0.25, 
 a sample size of 2,500 will assure a standard deviation of less than 
 0.01. But a smaller sample size may be used, depending on the magnitude 
 of the error rate. To determine this, select a small test sample, say 
 of 100 records. Estimate the error rate for this test sample. Then a 
 safe determination of an adequate sample size is given by 
 
 Sample size = 10,000 x estimate of error rate. 
 
 For example, if the estimate is 0.1, then the sample size should be 1000. 
 
 Having determined the error and omission rates, the confidence factors 
 (see Section 5.1) can be calculated. The recall ratio for any query in- 
 volving the key fields for which these confidence factors have been 
 developed is simply the product of the confidence factors. 
 
 50 
 
 Hoel , P. G. , Introduction to Mathematical Statistics , John Wiley and 
 
 Sons, New York, 1954, pp. 87-88, also pp. 107-108. This is the usual 
 
 formula for the standard deviation of a mean value. 
 
 65 
 
Assuming that the error and omission rates for the variables SURNAME, 
 STREET NUMBER, STREET NAME, and DATE OF BIRTH were 0.01, 0.03, 0.03, 0.01, 
 respectively, then the resulting confidence factors would be: 
 
 SURNAME 
 
 0.99 
 
 STREET NUMBER 
 
 0,97 
 
 STREET NAME 
 
 0.97 
 
 DATE OF BIRTH 
 
 0.99 
 
 and a query involving all four of these fields would tend to retrieve 
 0.99 x 0.97 x 0.97 x 0.99 = 0.922 or 92.2% of the relevant records. 
 
 Use of these data will be shown below. 
 
 5.5 PRECISION AND PROBABLE MATCH 
 
 Given a certain retrieved record, the question arises: Is it in fact 
 the correct record for the requester? The answer to this must be ex- 
 pressed as a probability. The theoretical formulation of this probabil- 
 ity problem is presented in Appendix II. 
 
 A computer program was written to calculate probability values resulting 
 from the use of various combinations of keys. Recall that the basic pro- 
 blem is to evaluate the probability that a record will match a requester 
 given the presence in the record of a number of key values K, , ..., K 
 which match the request. The probability values are developed in terms 
 of the following auxiliary quantities. 
 
 (1) p = the a priori probability that a randomly selected record 
 corresponds to that record desired by a requester. 
 
 (Notation in Appendix II, Equation (16), p = P(M. )) 
 
 The range of p should be from 1/T (where T is the largest population from 
 which the data base is selected) to 1/N (where N is the number of records 
 in the data base). 
 
 For each key j on which the search is conducted, let 
 
 (2) a. = error rate in key j (a proportion). 
 
 (3) b. = relative frequency of omission of data in key j. 
 
 J 
 
 From (2) and (3), we calculate 
 
 (4) c. - 1 - aj - bj . 
 Thus c. is the probability 
 
 66 
 
PCKjlM.) 
 
 given in Appendix II, Equation (8b). 
 
 (5) Let n. be the number of records with specific matching data in 
 key j, i.e., the number of records retrieved matching the re- 
 quested value of key j. Let N be the number of records in the 
 data base, then define 
 
 r. = n./N 
 
 Thus r. is the probability P(K.) given in Appendix II, p. 176. 
 
 j j 
 
 (6) For each key value j we define 
 
 s j = c o /r j- 
 
 (7) We then have from Appendix II, Equation (18), the probability 
 P(M. |K.) given by 
 
 (8) An auxiliary quantity F (Appendix II, Equation (23)) is then 
 calculated: 
 
 / ' \n-l n /l - q.\ 
 
 where n is the number of key values which match the request. The quan- 
 tity F is used as shown below. 
 
 (9) Finally, the required probability (i.e., the probability that 
 a record is that desired by a requester conditional on a match 
 on n key values in the query) is given by 
 
 V= l 
 
 1 + EF 
 
 where E is a quantization of a certain independence relation between the 
 keys, (see Appendix II, pp. 179-180). The smaller the value of E, the 
 greater the ability of the keys to discriminate. Independent keys mean 
 that E = 1. If E < 1, then V > 1/(1 + F). 
 
 The probability value V corresponds to the precision of a retrieval of 
 one record. 
 
 Results of the computer simulation are shown in Appendix I. The tables 
 should be read as follows: 
 
 67 
 
(1) Each table is identified by the data fields used in the 
 simulated query. All simulated queries utilized the sur- 
 name field. 
 
 (2) The surname decile identifies the discriminatory power of 
 the particular surname used in the query: Surnames in 
 the first decile are the most specific (TREFFTZS, for 
 example) while those in the tenth decile are the more 
 common surnames, such as JONES and SMITH. 
 
 (3) The column headings reflect the three sample file sizes 
 which were simulated: 1 million, 4 million, and 220 
 million records. 
 
 (4) The left column of the table reflects the recall factor* 
 obtained by multiplying together the simulated error rate 
 for each of the fields utilized in the query. These rates 
 were varied in parallel from (no errors) to 6% errors 
 
 in increments of 0.5%. All fields were assumed to be 
 subject to the same error. 
 
 (5) The body of each table tabulates the precision of each 
 retrieved result with respect to the query. Values of 
 1.0 imply that unique records were properly identified. 
 Values less than 1.0 imply that the probability of 
 unique identification is less than 100%. 
 
 The model reflects the precision and recall resulting from the attempt 
 to locate the record pertaining to a specific individual in a data base 
 of the specified size, subject to given error rates, for several types 
 of hypothetical queries. Three file sizes are postulated: a file of 
 220 million records, one of 4 million records, and a smaller file con- 
 taining only 1 million records. All the queries are assumed to consist 
 of a surname and one or more other key values. The discriminatory power 
 of a query varies with the discriminatory power of the key and key values 
 used and the confidence factor in the data base. The precision of a 
 retrieval made against the three file sizes given the retrieval confi- 
 dence factor of 92,2% in the example of Section 5.4, and utilizing the 
 four key fields of surname, street number, street name, and date of birth 
 can be read from the fifth line (recall 0.922) of Tables 5a - 5j in 
 Appendix I. The tables present 10 different values for the retrieval 
 precision of such a query, depending on the value of the surname: names 
 such as Jones have far less discriminatory power than names such as 
 Trefftzs or Krcmar. It can be seen that the precision of a query im- 
 proves as the file size decreases. Files smaller than 1 million records 
 will provide proportionately better results. 
 
 * 
 The definition of recall is given in Section 5.2, 
 
 68 
 
6.0 CONCLUSIONS AND RECOMMENDATIONS 
 
 6.1 CONCLUSIONS 
 
 The preceding sections have addressed the problem of accessing individual 
 records from personal data files using non-unique identifiers where 
 accessing includes updating, purging, and other bookkeeping tasks. The 
 value of the legislation requiring Federal agencies to make such records 
 available on request to individuals whose dossiers exist in their files 
 is obvious, but compliance with this legislation is a complex matter for 
 which there can be no single generalized approach. Each agency must 
 determine independently the most appropriate solution given its own 
 operational environment. 
 
 Exphasis in this document has been on one aspect of the required imple- 
 mentation: the retrieval of the individual's record through use of 
 identifiers that he can readily recall and that do not include his Social 
 Security account number. The analysis has included examination of the 
 characteristics of standard identifiers with emphasis on the varying 
 selectivity of these variables. The high discriminatory value of the 
 NAME variable resulted in inclusion of descriptions and evaluations of 
 several of the currently available name lookup algorithms. Techniques 
 applicable to this type of retrieval problem were discussed along with 
 indexing schemes ranging from the most straightforward to complex systems 
 of encoded pointers. 
 
 The selected examples for generating the precision tables from the mathe- 
 matical model (see App. I) clearly show that retrieval through use of 
 non-unique identifiers is, indeed, possible. It must be understood, 
 however, that the search keys used in these examples are illustrative 
 only. In practical cases it may be necessary to employ search keys with 
 more discriminating power. Furthermore, such retrieval has been carried 
 out in a variety of ways and has proved to be practical. For example, 
 the 3-level index scheme developed by the Immigration and Naturalization 
 Service presently retrieves 5,000,000 records (anticipated growth up to 
 10,000,000 records) with a response time per record measured in seconds. 
 
 The discussion of available techniques also made it clear that the degree 
 of success resulting from use of such techniques depends directly on the 
 quality of the data items being used as search keys. (The assumption 
 is made that the query data itself if correct and that it has been 
 entered properly.) The reliability of retrieval through use of non-unique 
 identifiers was further demonstrated through use of a probability model 
 programmed to generate the precision (i.e., the probability that a record 
 if retrieved as matching the query, as formulated, .on a variety of keys, 
 will be in fact the correct record), given a wide range of recall factors 
 (i.e., the probability that the record, if in the file, will be retrieved 
 based on a range of confidence factors simulating a reasonable level of 
 error and omission rates). Results from this modeling procedure again 
 indicate that retrieval mechanisms based on high discrimination keys 
 are practical . 
 
 69 
 
It is also clear, however, that these techniques must be implemented with 
 great care. The use of non-unique identifiers results in very high pre- 
 cision but rather low recall. This effect is obvious from considera- 
 tion of the fact that the more identifiers used, the more complete the 
 identification of the individual becomes. At the same time, the intro- 
 duction of more keys increases the potential for problems in matching 
 exactly the information that is contained in the file. For this reason, 
 a Soft-Match Technique, such as that described in Section 5, may be 
 necessary as back up to the retrieval procedure. 
 
 6.2 RECOMMENDATIONS 
 
 The Office of Management and Budget has interpreted the language of the 
 Privacy Act to mean that the mere existence of personal data items in a 
 file is not sufficient to bring it under these legislative constraints. 
 In accordance with the definition of "system of records" (Privacy Act, 
 Sec. 552a (a) (5)) these items become relevant only when they are used 
 as retrieval keys: 
 
 "This language further suggests that the Congress did not 
 intend to require that an individual be given access to 
 information which the agency does not retrieve by reference 
 to his or her name or some other identifying particular." 
 
 A first step in compliance will then be to determine the exact keys cur- 
 rently in use and from that list to determine whether or not the legis- 
 lation applies. 
 
 The current considerations apply only to the specific problem of the 
 retrieval of an individual's record. But even this single aspect of 
 the legislation will require a review of existing systems to make the 
 following determinations: 
 
 1. Are the personal identifiers contained in the file necessary? 
 
 2. What non-unique identifiers are currently available as 
 retrieval keys? 
 
 3. Are these currently available retrieval keys adequate for 
 accessing the required records in a cost effective way? 
 
 4. If not, how can the existing capabilities be expanded or 
 modified to make the required records accessible? 
 
 Each of these considerations is discussed below. 
 
 51 Office of Management and Budget Circular A-108 and accompanying 
 "Guidelines for Implementing Section 3 of the Privacy Act of 1974," 
 Federal Register , 9 July 1975, Vol. 40, No. 32, pp. 28948-28978. 
 
 70 
 
6.2.1 Evaluation of Current File Access Capability . If it does apply, 
 the agency should make a determination of acceptability of the current 
 retrieval capability in the context of the legislation and the 0MB Guide- 
 lines. Using the search keys that are both available and relevant, 
 weighting factors should be formed as outlined in Section 3. This pro- 
 cedure should be followed by an analysis of the error and omission rates 
 for these variables as suggested in Section 4. From these rates, confi- 
 dence factors can be formed, using the methodology described in Section 5, 
 and a determination of precision and recall made for some combinations 
 
 of these variables using the computer-generated tables shown in Appendix 
 I. If the variables that are already available as retrieval keys are 
 not adequate to the task, then current system capabilities will require 
 expansion. Some discussion of available techniques is given in Section 
 2 for consideration under these circumstances. It should be noted, how- 
 ever, that the Office of Management and Budget has stressed use of exist- 
 ing capabilities whenever possible. 
 
 "...the development of new retrieval and indexing capa- 
 bilities is not encouraged, rather agencies should ex- 
 ploit existing capabilities to serve individual needs. "52 
 
 6.2.2 Evaluation of the Data Base . The most difficult aspect of the 
 analysis procedure is that involving evaluation of the data base itself. 
 
 An orderly sequence of steps should be taken to evaluate any data base 
 in order to bring it into compliance with the requirements of the Privacy 
 Act. A similar sequence should be followed when establishing any new 
 data bases containing personal data. For existing data bases, the re- 
 quired actions fall into the categories of: 
 
 1. Analysis of present data validity, 
 
 2. Correction of errors, 
 
 3. Retirement of obsolete data, and 
 
 4. Establishment of a review cycle to keep data current. 
 
 For new data bases, the first of these steps is replaced by an analysis 
 of data requirements and initial data collection steps. 
 
 While it is unrealistic to assume that any data base is error-free in 
 any data field, all fields which comprise the data base should 
 contain data which are as accurate as possible. Error rates which ex- 
 ceed four or five percent tend to throw the utility of the data base into 
 question, and fields with such error rates should be either corrected 
 (at some cost) or deleted from the file. The basic test of any personal 
 data base is whether the people represented by the records in the data 
 base can, in fact, be found and shown to be the individuals to whom the 
 records are assumed to refer. As time passes, addresses change, 
 names change, ages change, occupations change, and people move. 
 
 M Loc.CitT 
 
 71 
 
Files which have not been subjected to ongoing verification and update 
 activities, particularly those in which the subjects are not actively 
 involved in the maintenance process, may be found to contain a large 
 cast of fictional characters. 
 
 The requirement for the maintenance of such files should be reviewed 
 at the highest levels—certainly such information should not be made 
 the basis of substantive policy decisions, and the \/ery need for the 
 file in question should be carefully considered. Files which are really 
 necessary, however, should be evaluated periodically to determine their 
 accuracy and to correct erroneous data. 
 
 The utility of such files--and compliance with privacy legislation- 
 requires that a high level of accuracy be maintained, and that suitable 
 resources be expended to insure such a level of accuracy. 
 
 72 
 
APPENDIX I 
 
 PRECISION TABLES 
 
 Table IDs Variables Selected Pages 
 
 la - lj Surname and Sex 74 - 83 
 
 2a - 2j Surname and Street Number 84 - 93 
 
 3a - 3j Surname and Birthdate (month/day) 94-103 
 
 4a - 4j Surname and Birthdate (year/month/day) 104 - 113 
 
 5a - 5j Surname , Birthdate (year/month/day), Street Name 
 
 and Street Number 114-123 
 
 6a - 6j Surname and State 124-133 
 
 7a - 7j Surname , Sex , and Birthdate (month/day) 134 - 143 
 
 8a - 8j Surname , Sex , and Birthdate (year/month/day) 144 - 153 
 
 9a - 9j ( Surname , Street Name , Street Number , and State . . . . 154 - 163 
 
 10a - lOj Surname , Sex , Street Name , Street Number , and 
 
 Bi rthdate" Ty"ear/month7diy) 164-173 
 
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APPENDIX II 
 
 i 
 
 A PROBABILISTIC FORMULATION OF THE NON-UNIQUE ACCESS PROBLEM 
 
 In this appendix, we describe the theoretical basis of the probability 
 model discussed in Section 5. The problem is to calculate the prob- 
 ability that a given retrieved record which has been accessed by a 
 combination of retrieval keys such as name, date of birth, etc., will 
 indeed correspond to an individual whose record is requested. It is to 
 be noted that this theoretical development is completely independent 
 of the retrieval algorithm itself. It supposes that a certain record 
 has been retrieved and that it has certain characteristics which match 
 characteristics known by the inquirer. The question is then: what is 
 the probability that it is in fact the record desired by the inquirer? 
 
 Note that there is some conceptual difficulty in even defining precisely 
 when a record is, in fact, the record of a particular person. This is 
 because the record may not uniquely characterize a person or may contain 
 erroneous information. There are also attributive values which carry 
 more weight than others in identifying a record-person correspondence 
 (e.g., name). For purposes of the discussion, however, we assume that 
 it is meaningful to say that a record matches or corresponds to an 
 individual, i.e., js_ the record of the individual. This proposition 
 or event we designate as 'M. '; that is, 
 
 M. = a given record matches the individual i /,\ 
 
 (e.g., who has attributes name = x,, address 
 = x«, ..., attribute. = x.) 
 
 The second event to be specified in the probabilistic formulation is that 
 a record contains a particular attribute value for the jth retrieval key: 
 
 K. = a given record has a particular attribute (2) 
 
 va 
 
 lue for key j (e.g., name = x,) 
 
 It is useful to define a third "reference" event that defines the universe 
 of discourse or context of the probability formulation. We call this A. 
 
 In what follows we can consider A to be the selection of a given record 
 from a data bank where the mode of selection is not specified. 
 
 The basic probability to be calculated is then 
 
 P(M. |Ki & K 2 & ... & K n & A) (3) 
 
 174 
 
where '&' indicates logical conjunction. Thus (3) is the probability 
 that a given record matches the individual i conditional on the fact 
 that the record contains certain attribute values on the set of keys 
 j = 1, ..., n. The basic problem of non-unique access is to select a 
 set of keys so that the probability (3) is near unity. 
 
 Let us first solve the problem of calculating probability (3) by study- 
 ing the case of a single key, i.e., the probability 
 
 P(M 1 |K J & A) (4) 
 
 By the multiplication principle we have 
 
 P(M, |A) P(K, |M, & A) 
 P(M.|K, & A) = 3 J-J (5) 
 
 i ' J 
 
 P(K,|A) 
 
 For readability we will omit the reference event A, thus (5) assumes the 
 simpler form. 
 
 P(M.) P(K-|M.) 
 P(M, |K.) = 3 LJ_ (6) 
 
 i ' J 
 
 P(K.) 
 
 The factors on the right have the following meaning: 
 
 P(M-) = the a priori probability that a (7) 
 
 1 randomly selected record matches 
 the individual i 
 
 P(M. ) = 1/N if N is the total population upon which the data bank is 
 drawn.* For example, if the individual i is known to be a California 
 driver and the data bank contains all and only California driver records, 
 then N = the size of the data bank. On the other hand, if the data bank 
 concerns information on adult U.S. citizens, then N = the number of adult 
 U.S. citizens. 
 
 * 
 
 In the complete formulation with the reference event A, A could be con- 
 sidered to contain the information regarding the population upon which 
 the data bank is based. 
 
 175 
 
We next have: 
 
 P( K - | M. ) = the probability that a record contains (8a) 
 a specific value for retrieval key j 
 conditional on the fact that the record 
 matches the individual i. 
 
 For example, if the j th key is i's address, then P(K. |M. ) is the prob- 
 ability that i's record contains his correct address. It therefore 
 follows that 
 
 P( K • | M . ) = the complement of the sum of the error (8b) 
 
 rate in key j and the relative frequency 
 
 of omission of data in key j 
 
 Note that for perfectly accurate records, we would have P(K- |M, ) = 1.* 
 
 The third factor P(K. ) is estimated as the relative frequency of the 
 specific information given by key j in the bank. For example, if two 
 records are retrieved having the same value for key j and m is the size 
 of the data bank then P(K-) is estimated as 2/m. 
 
 Now if a set of keys j = 1, ..., n are independent , the probability (3) 
 is calculated by putting the conjunction K & ... &K n for K- in (5) (or 
 the abbreviated Form (6)) and expanding the result into a product for 
 j = 1 , . . . , n ; i.e., 
 
 n P(K.|M.) 
 P(M. K,& ... &KJ = P(M.) IT J J (9) 
 
 However, the independence assumption is only valid for certain keys. For 
 example, name and birthdate can be regarded as independent, but not birth- 
 date and year of graduation from high school. Independence also depends 
 on the conditional probability. We therefore must look for a more general 
 solution. 
 
 k 
 
 The error rate in query formulation is not part of the probability (8)> 
 which has to do with the probability of the selection procedure being 
 successful conditional on an error-free query. 
 
 176 
 
One approach to this problem is to use some probability machinery 
 developed by W. E. Johnson and reported in Keynes.53 There are also 
 versions of the Johnson work in Carnap. 5 ^ 
 
 We now present our version of the so-called "Johnson Cumulative Formula." 
 A set of relevance coefficients or coefficients of influence is first 
 defined. For any propositions (events) K, , . . . , K , we define 
 
 P(K,|K ? & A) 
 
 C(K,,KjA) = i —^ (10) 
 
 1 2 P(K X |A) 
 
 C(K r K 2 ..., KJA) = C(K 1$ K 2 |A)-C(K 1 & K 2 , K3, ..., KjA) (11) 
 
 It is then easy to show that C(K, , ..., K |A) has the operator property* 
 
 P(Kj & ... & K n |A) = C(K r ..., KJA) JT P(Kj|A) (12) 
 (see Carnap55) # 
 
 >i J 
 
 The coefficient (11) is thus a coefficient of independence and is unity if 
 the events K, , K«, . ..» K are independent relative to the event A. 
 
 The cumulative formula is obtained as follows: 
 
 Let K be the conjunction K,& ... &K . Then by the multiplication principle 
 
 P(M. |A) P(K|M, & A) 
 
 P(M.|K&A)= ] (13) 
 
 1 P(K|A) 
 
 Applying (12) to the numerator and denominator of (13) and then multiply- 
 ing both sides of the result by [P(M i fA) ] n "l we get 
 
 * 
 We use our own notation for the coefficient. 
 
 53 
 
 Keynes, J. M. , A Treatise on Probability , MacMillan and Co., London, 1929, 
 
 pp. 149-155. 
 
 54 
 Carnap, R. , The Logical Foundations of Probability , University of Chicago 
 
 Press, 1950, pp. 356-360. 
 55 Ibid , p. 358, Formula T66-3C. 
 
 177 
 
(14) 
 
 n1 C(K,, ..., K n |M. & A) n / P(M. | A) P(K. |M & A) 
 [P^A)]"' 1 • P(M.|K & A) = — i H_J JT a 
 
 C(K r ..., KJA) j=l \ P(Kj |A) 
 
 We now use the cumulative formula (14) in the following way. First intro- 
 duce the abbreviations. 
 
 V = P(M.|K & A) (15) 
 
 P = P(M.|A) (16) 
 
 q j = P(M i |K j & A) (17) 
 
 By the multiplication principle, q. is the factor in the parentheses of 
 (14), i.e., J 
 
 P(MjA) P(K.|M. & A) 
 
 q, = ] J — ] (18) 
 
 J P(Kj|A) 
 
 Our problem is to study the expression for V. Using the abbreviations, 
 the formula (14) becomes 
 
 . C(K,, ..., K |M. & A) n 
 
 P n_1 v = — i Q— ! TT q, (19) 
 
 C(K X , ..., K n |A) i-1 J 
 
 We now use (19) again, but this time substitute '^M. ', i.e., the negation 
 of M-j , for 'Mj' in (14). The quantities p, V, and qj are then replaced 
 by their complements. We get. 
 
 n 1 C(K , ..., K KM. & A) n 
 
 (1-P) n_1 (1-V) - — i- n —± i TT (1 - q.) (20) 
 
 C(K p ..., K A) j=l J 
 
 178 
 
The ratio of (20) to (19) yields 
 
 --CfsR 1 ) 
 
 where E is the factor 
 
 C(K,, ..., KjoM, & A) 
 E = i 1 (22) 
 
 C(K r ..., K | M - & A) 
 
 The equation (21) is the solution to our problem; that is, the right side 
 of (21) is the product of the two quantities, E as defined by (22), and 
 
 so that (21) becomes 
 
 from which we derive 
 
 7 - I'" EF 
 
 v_I 
 
 1 ♦ EF < 24 > 
 
 We see that V depends not only upon the quantities q. but also on p, the 
 a priori probability of M. , and the quantity E. The J value used for E is 
 usually obtained by independence assumptions . 
 
 Note that if 
 
 E < 1 
 
 then, from (24), 
 
 179 
 
V 1 + F (25) 
 
 This means that a lower limit to the probability of M- conditional on the 
 combination K, & K~ & ... & K can be computed if E < 1. The value 
 E = 1 occurs when the numerator and denominator of (22) are equal. In 
 particular, this is the case when the keys are independent relative to 
 both~M- and M. . The smaller the value of E, the greater the ability 
 of the keys to 1 discriminate. 
 
 To summarize: We have three formulas for the desired probability V each 
 involving a different ratio of coefficients of influence: the formula 
 (19), the formula (20), and the formula (24). In Section 5 we have 
 implemented the formula (24) for a range of values of E. 
 
 180 
 
APPENDIX III 
 SOFT-MATCH TECHNIQUES 
 
 In Section 5.2, we noted the classical problem of trade-off between re- 
 call and precision, i.e., increasing recall lowers precision. But this 
 arises only in the case where all retrieved records are treated on an 
 equal par and not subjected to further filtering. Our approach to the 
 problem is to increase the recall and then to provide a mechanism for 
 additional filtering to improve the precision. In this section we dis- 
 cuss a method for increasing the recall called "Soft-Matching." The 
 result of a Soft-Match Technique is not only a set of retrieved records, 
 but also a numerical weight assigned to each record to indicate its 
 closeness to the query. As an example, consider the case of a query 
 which is a conjunction of three key values denoted by A, B, C. A tradi- 
 tional retrieval would select only records that matched on all three. A 
 soft-match technique might be to also select those records that matched 
 on A and B but not on C, on A and C but not on B, and on B and C but not 
 on A. These secondary retrievals would then be assigned weights less 
 than that assigned to a record that matched on all three. The retrieval 
 could also be extended to tertiary retrievals such as records that match- 
 ed on A but not on B and not on C, etc. These would be given the lowest 
 weights. 
 
 In this appendix a heuristic weighing scheme is presented for filtering 
 the candidate records. In Section 5.5 and Appendix II we presented a 
 second weighting scheme based on probabilities which is in fact the 
 measure of precision for a single retrieval. (That is to say, precision 
 is the proportion of retrieved documents that are relevent, i.e., match 
 the inquirer. Consequently, if exactly one record is retrieved, then the 
 precision corresponds exactly to the probability that the record is in 
 fact that desired by the inquirer.) 
 
 On the basis of the above discussion, we see that a soft-match technique 
 involves two components: (1) an algorithm for determining a subset of 
 the data base of manageable size which has maximum recall; (2) a scoring 
 function for assigning the closeness measures to each item in the subset. 
 
 First, consider the problem of determining a manageable subset. Suppose 
 that the retrieval prescription involves n keys 
 
 K| , K« j ...» K 
 
 Then let S^) be the set of records which match on the i th key. In 
 
 principle, the subset with maximum recall would be the set union of S(K,), 
 
 •••» S(K n ). This approach would not be efficient however, so a method 
 
 which utilizes the error and omission rates and the discrimination factors 
 is recommended. These quantities are defined as follows: 
 
 181 
 
J.L. 
 
 a. = probability that the key value for the i key is incorrect 
 
 b. = probability that the key value for the i key is omitted 
 
 e i ■ a i + b i 
 
 (Thus, the complement of e. is the quantity c. defined in Section 5.1). 
 
 The discrimination factor of a key is measured by the relative frequency 
 of a key value in the data base. Let R. be the relative frequency for 
 
 the i th key. This quantity R. is estimated for certain keys by the 
 
 values given in Tables 5.3-1, 5.3-2; it therefore corresponds to the 
 concept of retrieval ratio. 
 
 It follows that the initial selection of the retrieval should be the 
 union of, not all n sets S(K.), but a certain selection of fewer sets 
 
 say S(K. ), ..., S(K. ) (m<n), that has the properties 
 h n m 
 
 (1) R. x R. x ... x R. is less than a certain threshold r . 
 
 1 l m 
 
 (2) e. x e. x ... x e. is less than a certain threshold e . 
 
 h h \ ° 
 
 The first property guarantees that the retrieval set has manageable size. 
 The second property guarantees that the probability of missing a relevant 
 record is low. 
 
 The second aspect of the soft-match technique requires the definition of 
 the scoring function. For this purpose consider that we have an arbitrary 
 quantitative "strength of match" function for each key. For example, for 
 a key designating home address, one such function might be 
 
 f . = 1 for complete match 
 
 f. = .5 for agreement on the street name, part of the home 
 address but not on the house number part 
 
 f. = otherwise, 
 i 
 
 More complicated functions can be defined in obvious ways. For example, 
 if the house number partially matched, say it disagreed on one numeral 
 out of four, then we could score this as .75. The simplest function is 
 the binary function, '1' for match, '0' for no match. 
 
 In general, we see that a score for a key for which f.. > (i.e., a 
 partially matching key) should: 
 
 182 
 
(1) Increase as R. decreases 
 
 (2) Increase as f. increases 
 
 (3) Increase as a. (the error rate in the i key) decreases. 
 
 It is therefore plausible to take the contribution of the i key to the 
 total score (in the case that f . > 0) to be proportional to the quantity 
 
 f i <i - a i> 
 
 R. 
 
 l 
 
 since this is a simple expression that satisfies the requirements (1), 
 (2), and (3). 
 
 It is convenient to normalize the scores, however, so we will take the 
 constant of proportionality to be 1/R where 
 
 n 1 
 R = z -4- (l) 
 
 i=l i 
 
 Thus, the score for the i key is 
 
 1 f i (1 " a i } 
 
 S = — — — (2) 
 
 *1 R R. {d) 
 
 We now define the total score as consisting of two components: a positive 
 component made up of the contributions S. defined above and a negative 
 component to be defined below. ] 
 
 The positive component we take to be simply 
 
 ; i (3) 
 
 n 
 S = I 
 i=l 
 
 (Note that this summation runs over all the keys in the query, and not 
 over just the subset of m keys used to get maximum recall.) 
 
 By (1) and (2) we see that S is a convex linear combination of the terms 
 f.(l-a.) where i = 1, ..., n, and so S can range from to 1. 
 
 To define the negative component, we see that if a key is error free and 
 fails to match (f. = 0), then it should kill off the entire retrieval. 
 
 Thus, in the case f . = we should have a negative contribution which 
 
 183 
 
varies as (1 - a.)- We therefore take the score when f. = to be 
 i l 
 
 V-(l-a.) 
 
 Since a value of T.= -1 for only one key is sufficient to invalidate a 
 
 retrieval, we take the total negative score to be, not the sum of the 
 individual scores, but rather the negative of the maximum value of 
 (1 - a.), i.e. , 
 
 T = - max (1 - a. ) 
 i 
 
 f.=0 
 
 Thus T ranges from -1 to 0. 
 
 The retrieval scoring function is thus 
 
 U = S + T 
 
 An example of how this scoring function is used is given in Table III-l 
 The example deals with the scoring of four records based on values of 
 the parameters R. (the retrieval ratio) and a. (the error rate). 
 
 184 
 
Table III-l. Example of Soft-Match 
 
 
 KEY 
 
 
 NAME 
 
 ADDRESS 
 
 SEX 
 
 R i 
 
 .003 
 
 .00002 
 
 .5 
 
 a i 
 
 .001 
 
 .01 
 
 
 
 Table of Values for R. , a. 
 
 
 NAME 
 
 ADDRESS 
 
 SEX 
 
 Record 1 
 
 match (1) 
 
 match (1) 
 
 no match (o) 
 
 Record 2 
 
 match (1) 
 
 partial match ( 
 
 .75)* 
 
 match (1) 
 
 Record 3 
 
 partial match 
 
 (.83)** 
 
 match (1) 
 
 match (1) 
 
 Record 4 
 
 match (1) 
 
 match (1) 
 
 data absent 
 
 Table of Retrieval Outcomes 
 
 (values in parentheses are those for f. ) 
 
 
 S 
 
 T 
 
 U 
 
 Record 1 
 
 .9900 
 
 -1 
 
 -.0100 
 
 Record 2 
 
 .7442 
 
 
 
 .7442 
 
 Record 3 
 
 .9889 
 
 
 
 .9889 
 
 Record 4 
 
 .9900 
 
 
 
 .9900 
 
 Retrieval Scores 
 
 * The partial match example is for the case that one character 
 out of four doesn't match. 
 ** The partial match example is for the case that one character 
 
 out of six doesn't match, 
 
 185 
 
APPENDIX IV 
 FORTRAN SOURCE CODE FOR THE PROBABILITY MODEL 
 
 186 
 
FORTRAN IV 
 
 D06-31 SOURCE LISTING 
 
 PAGE 001 
 
 8001 
 0082 
 8883 
 8004 
 0885 
 8886 
 8887 
 8088 
 
 0009 
 
 00)0 
 
 08] i 
 
 0012 
 
 80 11 
 
 C QUERY SIMULATES THE DATA BASE/USER INTERACTION 
 
 DIMENSION A< 1 1) .B(ll) ,CC 1 1),RC i 1),S< 11) 
 DIMENSION JARYCi 1), IFLD(ll) 
 
 DIMENSION ARGS CI 1) 
 DIMENSION SURNMC 18) 
 
 COMMON IRAN. JRAN, EARYC27) , PARYC3) 
 DATA ARG3 ( 1 ) /. 23972SE-2/, ARGS C2) /. 8333 
 DATA ARGS C 4) / . 02/, ARGS <S) / . 5/, ARGS C 6 ) / 
 DATA ARGS C 8) /. 066666b/, ARGS (9) / 
 DATA SURNMC 1 ) .. SURNM(2) . SURNMC3) 
 
 3E- 1/, ARGS < 3) / . B2/' 
 0001/', ARGS (?>/. 0002/ 
 
 00Q333333/. ARGS ( 18) /. 555555E-4/ 
 SURNMC4)/.98?E-?, . 2459E-5, .9635E- 
 
 2 , .272037E-4/ 
 DATA SURNMC 5) 
 DATA SURNMC 8) 
 DATA 
 DATA 
 
 ,SURNMC6).SURNMC7)/-4.81966E-5,S,627E-5, 1.265E-4' 
 . SURNMC 9) , SURNMC 1 B) /4.8 197E-4, 7 . 832E-4, . 3384E-2- 
 P AR Y CI), PAPY (2) , PARY C 3 ) /4 . 545454E-9, 2 . 5E-7 , 1 . E -6- 
 E AR Y k 1 ) , EAR Y< 2 ) , EAR Y C 3 ) , E AR Y C 4) / . 5 , 1 . , 2 . , 1 , / 
 
 Q >K>K>t<>t"+ ■■■*■*■ H:***********^ 
 
 c 
 
 QUERY SIMULATES QUERY PROCESSING AGAIN; 
 IT GENERATES VALUES FOR ACJ)CTHE ERROR 
 
 . I A DA l A BASE 
 RATE IN A FIEt 
 
 ,D3 
 
 BCJ) CTHE OMISSION PATE OF DATA IN A FIELD] 
 
 C C J ) L 1 - C A C J ) +0 C J ) ] . . . THE COHF I DENCE FACTOR 
 
 RCJ) THE PERCENTAGE OF THE FILE RETRIEVED VIA A FIELD, AND 
 
 SCO A QUERY SCORE, WHICH RANGES FROM 1 TO THE FILE SIZE. 
 
 SCJ) IS NOMINALLY THE RATIO CCJ)/RCJ), AND IS ADJUSTED HERE 
 
 TO MAINTAIN REASONABLE LIMITS. 
 
 THE USER ENTERS THE NUMBER OF ARGUMENTS CFROM 1 
 
 UHICH HE LUSHES TO USE IN HIS QUERY. THE QUERY 
 
 IT REQUIRED A SUCCESSFUL BOOLEAN AND OF ALL THE 
 
 SUCCESSFUL. 
 
 THE PROGRAM LULL TERMINATE WHEN THE NUMBER OF INPU 
 
 IS SET TO ZERO. 
 
 TO 11) 
 
 IS TREATED AS 
 
 ARGUMENTS TO 
 
 ARGUMENTS 
 
 IF 
 BE 
 
 FOR THIS TEST THE VALUES OF A C.J) AND BCJ) WILL BE TREATED AS ZERO, 
 FOLLOUING THE ENTRY OF THE FIRST NUMBER— THE NUMBER OF ARGUMENTS 
 TO FOLLOU--THE USER MAY ENTER NUMBERS CORRESPONDING TO THE ACTUAL 
 ARGUMENTS. EACH ARGUMENT HAS AN ASSOCIATED RCJ), UHICH IS THE 
 PERCENTAGE RETRIEVAL TO BE EXPECTED. THE ARGUMENT NUMBERS AND 
 THEIR ASSOCIATED VALUES ARE AS FOLLOWS: 
 
 C ARGUMENT NUMBER 
 
 MEANING 
 
 RETRIEVAL RATIO 
 
 0814 
 
 C 
 
 C 9 
 
 C 18 
 
 C BEGIN BY ASKING FOR 
 
 1 JRITE (6,285) 
 
 C NOW EXAMINE HARGS TO 
 
 DATE OF BIRTH CMMDD) 
 
 MONTH OF BIRTH 
 
 YEAR OF BIRTH 
 
 STATE 
 
 SEX 
 
 STREET NUMBER 
 
 STREET NAME 
 
 EDUCATION 
 
 COUNTY 
 
 DATE OF 
 
 1/365 
 1 / 1 2 
 1/50 
 1/58 
 
 1 / i , 
 
 i /5 , e 
 
 1 / 1 5 
 i/3800 
 
 BIRTH CYMD) 
 OPERATOR INPUT. 
 
 START THINGS GOING. 
 
 000 
 
 00 
 
 C EST I MATED) 
 
 1/18,098 
 
 187 
 
FORTRAN IV D0G-01 SOURCE LISTING PAGE 002 
 
 £ X; >f~K ?t "S' H< >H t< ?f- >K >K ?fc sfof« 5fc ■+-' ^foto+of- ' 
 
 00 i 5 READ (6,216) HARGS 
 
 00 16 If- '' NARGS) 160, 100, 10 
 
 C COME HERE TO PREPARE A QUERY SURROGATE. 
 
 06 J ? 1 DO 20 I = i . NARGS 
 
 0016 WRITE (6,220') 
 
 0019 READ (6.210) J 
 
 0020 A ( I) =0 . 
 
 0021 B( I) =8 . 
 0622 C(I)=1. 
 0021 R ( I ) =ARG5 (J) 
 
 0024 9( I) =f"C I) /R( I) ) 
 
 0025 JARY(I)=J 
 002b 20 CONTINUE 
 0027 WRITE (6,2i5) 
 
 C NOU CALL ON THE MAIN COMPUTATIONAL ROUTINE TO PREPARE THE TABLES 
 
 JARY(HARGS+i) =1 1 
 
 H=NARCP+1 
 
 UP 1 TE ( 5 , 2O0 ) NARGS 
 
 DO 25 J=l, NARGS 
 
 WRITE (5, 245) J, A (J) , J, B ( J) , J,C( J) , J,R ( J) , J,SC\J) , J, JARY(J) 
 
 CONTINUE 
 
 DO 50 1 = 1 , 10 
 
 COHARGS+l 1 ' = 1. 
 
 B (NARGS+1 ) =0. 
 
 A(NARGS+1 ) ~3 . 
 
 R(HARGS-M) =SURNM( I) 
 
 SCNARGS+1) =C(HhRGSM)/R(NARGS+1) 
 
 WR ITE(5,250) 
 
 DO 45 J=i .NARGS 
 
 GOTO < 3 1 .■ 32 .- 33 , 34, 35 , 36 , 3? - 30 , 39 , 40 > JAPY( J ) 
 
 WRITE (5, 255) 
 
 GOTO 45 
 
 URITE(5, 260) 
 
 GOTO 45 
 
 WRITE (5, 265') 
 
 GOTO 45 
 
 WRITE (5, 276) 
 
 GOTO -45 
 
 WRITE (5, 275) 
 
 GO I 45 
 
 UR I TEC 5 .2SQ) 
 
 GOTO 45 
 
 LIR ITE(5,285) 
 
 GOTO 45 
 
 WRITE (5, 290) 
 
 GOTO 45 
 
 WR ITFC5.. 295) 
 
 GOTO 45 
 
 WRITE (5, 360) 
 
 CONTINUE 
 
 WRITE (5, 305) 
 
 WR ITE(5, 150) 
 
 WRITE (5,225) I 
 
 WRITE (5, 230) 
 
 188 
 
 0028 
 
 
 0029 
 
 
 0030 
 
 
 007 1 
 
 
 0032 
 
 
 0033 
 
 25 
 
 0O34 
 
 
 0035 
 
 
 0036 
 
 
 0037 
 
 
 0038 
 
 
 0039 
 
 
 0040 
 
 
 0041 
 
 
 0042 
 
 
 0043 
 
 31 
 
 0044 
 
 
 0045 
 
 32 
 
 0046 
 
 
 O047 
 
 ~k"Z 
 
 48 
 
 
 O042 
 
 34 
 
 0050 
 
 
 005 1 
 
 35 
 
 0052 
 
 
 0053 
 
 36 
 
 0054 
 
 
 0055 
 
 ~\~ : ' 
 
 6056 
 
 
 0057 
 
 *iH 
 
 O058 
 
 
 0059 
 
 39 
 
 0060 
 
 
 Q06 1 
 
 40 
 
 OOfc? 
 
 45 
 
 O063 
 
 
 0064 
 
 
 0G65 
 
 
 OO G6 
 
 
FORTRAN IV 
 
 DOb-Sl SOURCE LISTING 
 
 PAGE 003 
 
 0667 
 6068 
 0869 
 
 0070 
 
 037 1 
 6072 
 
 0073 
 
 0074 
 
 0875 
 0876 
 0077 
 0078 
 0073 
 
 0080 
 
 WRITE' 5.. 235) 
 WRITE (5,248) 
 
 CALL P VC Y( ft , S . C , R , S.. N , JAR Y) 
 58 CONTINUE 
 
 GO TO i 
 100 CALL PVCY(A,B*C,R,S,HARGS, JARY) 
 
 CALL EXIT 
 
 C FORMAT STATErENTS 
 
 153 FORMAT (1H , i3X, 1 10 ('-'). 7X) 
 
 200 FORMAT ( ihi . 'NARGS=' , 12) 
 
 205 FORMAT ('8 ENTER NUMBER 
 
 213 FORMAT (12) 
 
 215 FORMAT (' EXITING QUERY") 
 
 220 FORMAT (* ENTER QUERY FIELD IDENTIFIER') 
 
 225 FORMAT ( 1H , 13X, * ! ' , 108X, ' ! ' , 7X/1H , i3X, * 
 
 1 = SURNAME DECILE' , iX, 12, 4SX, ' ! * , 7X) 
 
 OF ARGUMENTS '•'} 
 
 43 X, 
 
 0081 
 
 230 
 
 FORMAT'. 1H 
 
 , 13X, ' !' , iOBC-' ) , ' ! " ,7X/ 
 
 
 
 1 iH » i3X, ' 
 
 J * , 3X, ' ERROR RATE ' , 2X, ' !' , 2X, * RECALL FACTOR' , 
 
 
 
 2 2X, ' ! •' ,28 
 
 X, 'FILE SIZE',37X,' !' ,7X) 
 
 O082 
 
 235 
 
 FORMAT ( iH 
 
 . i3X, ' ! ' , ibX, ' ! ' , i7X, ' !* ,74('-' >,* ! * , <X/ 
 
 
 
 i 1H , 13X, ' 
 
 ! ' , 15X, ■' ! ' , i ?X. ' ! ' , 7X. ' 1 MILL ION ' , 7X, ' ! ' , 10X, 
 
 
 
 2 '4 MILL I C 
 
 N '' , 6X, ' ! * , 5X, ' 226 M I LL ION ' , 6X, * ! ' , 7X) 
 
 0083 
 
 240 
 
 FOPMATC IH 
 
 , 13X, ' ! ' , 108 ('-'), " !',7X> 
 
 0084 
 
 245 
 
 FORMAT( iHO 
 
 . 'AC , 12, ' ) =' , tib.8,3X, 'BC , 12, ' ) =' ,hib.3. 
 
 
 
 l 3X, ' C (' , I 
 
 2, ■ ) =' ,E15.8,3X, ■RC , 12, ' ) =' ,E15.8,3X, ' S c. ' , 
 
 
 
 2 12, ' ) ** .E15.8..3X, ' JARYC , 12, ' ) =' , 12) 
 
 0035 
 
 250 
 
 FORMAT ( 1H1 
 1 FIELDS') 
 
 ,43X, 'PRECISION OF RETRIEVED RESULTS FOR DATA 
 
 0086 
 
 255 
 
 FQRMATC IH 
 
 , 48X, 'DATE OF BIRTH (MONTH AND DAY ONLY)') 
 
 0087 
 
 260 
 
 FORMAT ( iH 
 
 , 48 X , ' MO hi TH OF B I R TH ' ) 
 
 0088 
 
 265 
 
 FORMAT ( IH 
 
 , 48X, ' YEAR OF B IRTH * ) 
 
 0089 
 
 278 
 
 FORMA T(1H 
 
 ,48X, 'STATE') 
 
 8896 
 
 275 
 
 FORMAT( IH 
 
 ,48X, 'SEX' ) 
 
 009 1 
 
 260 
 
 FORMAT ( lH 
 
 ,48X, 'STREET NUMBER') 
 
 6692 
 
 295 
 
 FOPMAT( IH 
 
 ,48X, "STREET NAME' ) 
 
 0093 
 
 290 
 
 FORMAT ( IH 
 
 ,4BX, 'EDUCATION' ) 
 
 0094 
 
 295 
 
 FORMAT ( iH 
 
 , 48X, ' COUNTY' ) 
 
 0095 
 
 300 
 
 FORMAT''. iH 
 
 , 48X, 'DATE OF BIRTH (YEAR, MONTH, AND DAY)''' 
 
 0096 
 
 365 
 
 FORMATC iH 
 
 ,48X, 'AND SURNAME'' 
 
 0097 
 
 
 END 
 
 
 189 
 
FORTRAN IV STORAGE MAP 
 
 A 008886 REAL #4 ARRAY (It) 
 
 B 088062 REAL*4 ARRAY (11) 
 
 C 6013136 REAL*4 ARRAY (11) 
 
 R 888212 REAL #4 A R R A Y (11) 
 
 S 888256 REAL-K ARRAY (11) 
 
 J A R Y 8 8 3 42 I N TEG E R *2 A R R A Y (11) 
 
 IFLD 888378 INTEGERS ARRAY (11) 
 
 ARGS 888416 REAL+4 ARRAY (11) 
 
 SURNM 008472 REAL>k4 ARRAY (18) 
 
 H A R G S 8 Q 2 1 8 I H TE G E R *2 VA R 1 AB L E 
 
 I 882 i 82 INTEGERS VARIABLE 
 
 J 002184 INTEGERS VARIABLE 
 
 H 882106 IHTEGER*2 VARIABLE 
 
 PVCY 008088 REAL*4 PROCEDURE 
 
 EXIT 080888 REAL +4 PROCEDURE 
 
 COMMON BLOCK // LENGTH 000174 
 
 INTEGERS VARIABLE 
 IHTEGER*2 VARIABLE 
 REAL*4 ARRAY (27) 
 REAL*4 ARRAY (3) 
 
 IRAN 
 
 680008 
 
 JRAN 
 
 080882 
 
 EARV 
 
 888804 
 
 PAR'r' 
 
 888 160 
 
 190 
 
FORTRAN IV DB6-81 SOURCE LISTING PAGE 081 
 
 C MAIN COMPUTATION ROUTINE OF PRIVACY .MODEL—MARCH 1976 
 
 **NOTE: THIS SUBROUTINE HAS BEEN MODIFIED TO PRODUCE A TABULAR PRINTOUT. 
 
 803 i SUBROUTINE PVCY(AJ . BJ, CJ, RJ, SJ, NARGS.. JARY) 
 
 C DECLARE VARIABLES 
 
 62 D I ME N S 1 hi J A R Y ( 1 D 
 
 0903 DOUBLE PRECISION B IGPH 1 15 .F,VARYC 13. 3) 
 
 0004 COMMON J RAN , -IRAN .. EAR Y( 27 ) , PARY ( 3 ) 
 
 0005 REHL AJ ( 1 1 ) , BJ ( 1 1 ) , CJ Cl 1) , RJ ( 11 ) , SJ ( 1 1 ) - P 
 
 0006 REP 1 . QARYcii) 
 
 O 7 D ATA VA R Y i 1 , 1 ) . '9 . D 6 / 
 
 |J^h>KJK?K?fC>',<.'-;'-~tc:i<>fiH;?f-^>l'it<?K»<»<>K;»< 
 
 C MAIN LOOP STARTS HERE 
 
 C 
 
 C VALUES ARE RETURNED IN THE ARGUMENT ARRAYS— TERMINATE IF NARGS< = 8 
 
 OOOS IF(NAPGS) 95,95.. 11 
 
 C BEG 7 N PROCESSING THE QUERY RESPONSE HERE 
 
 l ^U09 i I CONTINUE 
 
 H:j 10 DO 90 JK" i .. 13 
 
 00 11 L=.TK 
 
 00 1 2 CONF =1. _ 0. 085* C L - 1 ) 
 00 1 3 PEF AC =C ONE** ( NARGS ) 
 80 i4 ERRAT= I . -CONF 
 
 00 i 5 PERERR=ERRAT*l88 
 
 0015 DO SO I - i , 3 
 
 0*17 P=PHKY(I) 
 
 08 i 6 DO 20 1 1 = i , NARGS 
 
 88 1 9 20 OAR Y ( I J ) =p*S J ( I I ) *€ONF 
 
 C NOU COMPUTE THE FUNCTION F 
 
 C>^*?M ! :>t<>M!>i<>lo|«ts>M©(<>|0|©t<>t<>fc 
 
 0020 F-l. 
 
 002 1 X=P/< i . -F) 
 
 8822 DO 30 JJ=1 -NARGS 
 
 9823 BIGPI f JJ"' = ( 1 .-QARYCJJ; >/DARY(JJ) 
 
 00 24 3 G F = F *>v KB I G P I (J J > 
 
 8825 F=F/X 
 
 C PREPARE THE PRINTOUT TABLE 
 
 B 2 S V A R Y ( J !< .• I ''' = i . " ( 1 . +F ) 
 
 3027 IF(VARYCJK, T) .GT. (l.On VARYCJK, I) = 1 . 8 
 
 dy^y 80 i.:ON i INUE 
 
 0030 URITE<5, 150) PERERR, REFAC. <VARY(JK, I) . i=3, 1,-1) 
 
 0031 90 CONTINUE 
 
 00 32 WRITE (5. 176) 
 6833 RETURN 
 
 C EXIT VIA HERE UHFH HftRGS-0 
 
 C**-*' 1 ^**?! •■Y.?¥-'* : '#''¥.':->?-:-'i¥W ¥■''«>¥. 
 
 0034 9 5 UR IT E C 5 , 288 ) 
 
 8835 RETURN 
 
 191 
 
FORTRAN IV D86-01 SOURCE LISTING PAGE 802 
 
 C F 'MAT 6 TATE MEN T SECTION 
 
 8036 t-riS FORMAT UH , i3X, ' i ' , 15X, ' f ' , 17X, * I " , 23X. ' .{ ' , 27X, ' ! * , 22X, ' ! ' , ?XS 
 1. !H , )3X, ' ! ' ,4X,FG.2, **' , 4X., ' ! ' ,4X,F8.4,5X, ' ! '* , 4X, 
 ? Ei5.8,4X, " i ' ,8X,Eib.8,6X, ' i ' , 3X, E 15 . 8,4X, ' ! ' , 7X) 
 
 0637 i70 FORMATUH .- i3X, i 18 ( ' -* ) , 7X) 
 
 £1038 260 FORMAT <1 HO,' NORMAL TERMINATION') 
 
 0038 END 
 
 192 
 
FORTRAN IV STORAGE MRP 
 
 .JhPV 668838 INTEGER*2 PARAMETER ARRAY (11) 
 
 BIGPI 
 
 y&0832 
 
 VARY 
 
 060 i 62 
 
 AT 
 
 8886 i4 
 
 B.J 
 
 6868 ib 
 
 CJ 
 
 888020 
 
 RJ 
 
 888022 
 
 3 J 
 
 1*60824 
 
 QARY 
 
 ■366652 
 
 NARGS 
 
 8 nm? 2 6 
 
 F 
 
 06 L 140 
 
 P 
 
 801 150 
 
 JK 
 
 601 154 
 
 L 
 
 081 i5b 
 
 COHF 
 
 001 166 
 
 REFAC 
 
 86 i 164 
 
 ERRAT 
 
 06 1 176 
 
 PERERR 
 
 661 174 
 
 I 
 
 66 i 266 
 
 II 
 
 66 i^e^ 
 
 X 
 
 06 i 264 
 
 J J 
 
 00 1210 
 
 REAL*8 
 
 ARRAY (.11) 
 
 i 
 
 REAL*8 
 
 ARRAY (13, 
 
 , 3) VECTORED 
 
 REAL+4 
 
 PARAMETER 
 
 ARRAY an 
 
 REAL*4 
 
 PARAMETER 
 
 ARRAY (11) 
 
 REAL*4 
 
 PARAMETER 
 
 ARRAY (11) 
 
 REAL*4 
 
 PARAMETER 
 
 ARRAY i i i ) 
 
 REAL*4 
 
 PARAMETER 
 
 ARRAY (11) 
 
 REAL ^4 
 
 ARFA ■:" ( i i) 
 
 
 INTEGER*2 
 
 PARAMETER 
 
 VARIABLE 
 
 REALMS 
 
 VARIABLE 
 
 
 REAL ^4 
 
 VARIABLE 
 
 
 INTEGER*2 
 
 VARIABLE 
 
 
 INTEGERS 
 
 VARIABLE 
 
 
 REAL*4 
 
 VARIABLE 
 
 
 REAL*4 
 
 VARIABLE 
 
 
 REAL*4 
 
 VAR I ABLE 
 
 / 
 
 REAL*4 
 
 VARIABLE 
 
 
 INTEGER*2 
 
 VARIABLE 
 
 
 IHTEGER'i'2 
 
 VARIABLE 
 
 
 REAL*4 
 
 VARIABLE 
 
 
 INTEGER*2 
 
 VARIABLE 
 
 
 :OMMON BLOCK // LENGTH 086174 
 
 INTEGER*2 VARIABLE 
 
 I NTEGER*2 VAR I ABLE 
 REAL*4 ARRAY (27) 
 REALK4 ARRAY (3) 
 
 ERAH 
 
 089890 
 
 JRAN 
 
 8W8882 
 
 EARY 
 
 666004 
 
 PARY 
 
 606166 
 
 193 
 
NBS-114A (REV. 7-73) 
 
 U.S. DEPT. OF COMM. 
 
 BIBLIOGRAPHIC DATA 
 
 SHEET 
 
 1. PUBLICATION OR REPORT NO. 
 
 NBS SP-500-2 
 
 2. Gov't Accession 
 No. 
 
 3. Recipient's Accession No. 
 
 4. TITLE AND SUBTITLE 
 
 Accessing Individual Records From Personal 
 Data Files Using Non-Unique Identifiers 
 
 5. Publication Date 
 
 February 1977 
 
 6. Performing Organization Code 
 
 7. author(S) Gwendolyn B. Moore, John L. Kuhns, 
 
 Jeffrey L. Trefftzs. Christine A. MontnorrexY- 
 
 G ORGANIZATION NAME ANlD ADDRESS 
 
 8. Performing Organ. Report No. 
 
 9. PERFORMING 
 
 Operating Systems, Inc. 
 21031 Ventura Boulevard 
 Woodland Hills, California 91364 
 
 10. Project/Task/Work Unit No. 
 
 640.1118 
 
 11. Contract/Grant No. 
 
 5-35928 
 
 12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP) 
 
 National Bureau of Standards 
 Department of Commerce 
 Washington, D.C. 20234 
 
 13. Type of Report & Period 
 Covered 
 
 Final 31 March 1976 
 
 14. Sponsoring Agency Code 
 
 IS. SUPPLEMENTARY NOTES 
 
 Library of Congress Catalog Card Number: 76-57950 
 
 16. ABSTRACT (A 200-word or less (actual summary of most significant information. If document includes a significant 
 bibliography or literature survey, mention it here.) 
 
 The Privacy Act of 1974 places restrictions on the Federal, state and local agencies 1 
 use of the Social Security account number as an identifier. For some agencies, 
 compliance will involve changes in implementation of retrieval algorithms. This 
 report describes methodology applicable to these changes in the more general context 
 of the problem of retrieving individual records from files using non-unique 
 identifiers. State-of-the-art retrieval techniques are discussed, a method for 
 assigning reliability weights to various personal data elements is presented, file 
 validation techniques for the error and omission rates of data items are suggested, 
 and a retrieval probability model — designed to show likelihood of retrieval of a 
 subject's record given a variety of populations, combinations of identifiers, and 
 error/omission rates--is described. A methodology is developed for forming confi- 
 dence factors from the established error/omi'ssion rates for combinations of non- 
 unique identifiers that are candidates for use as retrieval keys. Use of these 
 confidence factors as indices into the precision tables produced by the probability 
 model is described. 
 
 17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper 
 name; separated by semicolons) 
 
 Data retrieval; file validation; name lookup; non-unique identifiers; personal 
 data files; Privacy Act; probability model; retrieval. 
 
 18. AVAILABILITY f)(~l Unlimited 
 
 ! For Official Distribution. Do Not Release to NTIS 
 l_2f Order From Sup. of Doc, U.S. Government PjLntuift^ftffi 
 
 uraer r rom sup. ot uoc, u.;>. Government fjLQtu3a,utttce 
 Washington, D.C. 20402, SD Cat. No. C13 « i-V'.QVV-d 
 
 _J Order From National Technical Information Service (NTIS) 
 Springfield, Virginia 22151 
 
 19. SECURITY CLASS 
 (THIS REPORT) 
 
 UNCLASSIFIED 
 
 20. SECURITY CLASS 
 (THIS PAGE) 
 
 UNCLASSIFIED 
 
 21. NO. OF PAGES 
 2 03 
 
 22. Price 
 $2.65 
 
 USCOMM.DC 29042-P74 
 
 »U.S. GOVERNMENT PRINTING OFFICE: 1977 240-848/120 1-3 
 
ANNOUNCEMENT OF NEW PUBLICATIONS ON 
 COMPUTER SCIENCE & TECHNOLOGY 
 
 Superintendent of Documents, 
 Government Printing Office, 
 Washington, D. C. 20402 
 
 Dear Sir: 
 
 Please add my name to the announcement list of new publications to be issued in 
 the series: National Bureau of Standards Special Publication 500-. 
 
 Name 
 
 Company. 
 Address _ 
 
 City State Zip Code 
 
 (Notification key N-503) 
 

NATIONAL BUREAU OF STANDARDS 
 
 The National Bureau of Standards 1 was established by an act of Congress March 3, 1901. 
 The Bureau's overall goal is to strengthen and advance the Nation's science and technology 
 and facilitate their effective application for public benefit. To this end, the Bureau conducts 
 research and provides: (1) a basis for the Nation's physical measurement system, (2) scientific 
 and technological services for industry and government, (3) a technical basis for equity in trade, 
 and (4) technical services to promote public safety. The Bureau consists of the Institute for 
 Basic Standards, the Institute for Materials Research, the Institute for Applied Technology, 
 the Institute for Computer Sciences and Technology, and the Office for Information Programs. 
 
 THE INSTITUTE FOR BASIC STANDARDS provides the central basis within the United 
 States of a complete and consistent system of physical measurement; coordinates that system 
 with measurement systems of other nations; and furnishes essential services leading to accurate 
 and uniform physical measurements throughout the Nation's scientific community, industry, 
 and commerce. The Institute consists of the Office of Measurement Services, the Office of 
 Radiation Measurement and the following Center and divisions: 
 
 Applied Mathematics — Electricity — Mechanics — Heat — Optical Physics — Center 
 for Radiation Research: Nuclear Sciences; Applied Radiation — Laboratory Astrophysics " 
 — Cryogenics 3 — Electromagnetics s — Time and Frequency 2 . 
 
 THE INSTITUTE FOR MATERIALS RESEARCH conducts materials research leading to 
 improved methods of measurement, standards, and data on the properties of well-characterized 
 materials needed by industry, commerce, educational institutions, and Government; provides 
 advisory and research services to other Government agencies; and develops, produces, and 
 distributes standard reference materials. The Institute consists of the Office of Standard 
 Reference Materials, the Office of Air and Water Measurement, and the following divisions: 
 
 Analytical Chemistry — Polymers — Metallurgy — Inorganic Materials — Reactor 
 Radiation — Physical Chemistry. 
 
 THE INSTITUTE FOR APPLIED TECHNOLOGY provides technical services to promote 
 the use of available technology and to facilitate technological innovation in industry and 
 Government; cooperates with public and private organizations leading to the development of 
 technological standards (including mandatory safety standards), codes and methods of test; 
 and provides technical advice and services to Government agencies upon request. The Insti- 
 tute consists of the following divisions and Centers: 
 
 Standards Application and Analysis — Electronic Technology — Center for Consumer 
 Product Technology: Product Systems Analysis; Product Engineering — Center for Building 
 Technology: Structures, Materials, and Life Safety; Building Environment; Technical Evalua- 
 tion and Application — Center for Fire Research: Fire Science; Fire Safety Engineering. 
 
 THE INSTITUTE FOR COMPUTER SCIENCES AND TECHNOLOGY conducts research 
 and provides technical services designed to aid Government agencies in improving cost effec- 
 tiveness in the conduct of their programs through the selection, acquisition, and effective 
 utilization of automatic data processing equipment; and serves as the principal focus within 
 the executive branch for the development of Federal standards for automatic data processing 
 equipment, techniques, and computer languages. The Institute consists of the following 
 divisions: 
 
 Computer Services — Systems and Software — Computer Systems Engineering — Informa- 
 tion Technology. 
 
 THE OFFICE FOR INFORMATION PROGRAMS promotes optimum dissemination and 
 accessibility of scientific information generated within NBS and other agencies of the Federal 
 Government; promotes the development of the National Standard Reference Data System and 
 a system of information analysis centers dealing with the broader aspects of the National 
 Measurement System; provides appropriate services to ensure that the NBS staff has optimum 
 accessibility to the scientific information of the world. The Office consists of the following 
 organizational units: 
 
 Office of Standard Reference Data — Office of Information Activities — Office of Technical 
 Publications — Library — Office of International Relations — Office of International 
 Standards. 
 
 1 Headquarters and Laboratories at Gaithersburg. Maryland, unless otherwise noted; mailing address 
 Washington, DC. 20234. 
 
 - Located at Boulder, Colorado 80302. 
 
U.S. DEPARTMENT OF COMMERCE 
 National Bureau of Standards 
 
 Washington. DC. 20234 
 
 OFFICIAL BUSINESS 
 
 Penalty for Private Use. $300 
 
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