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A1 1103089142 CE & TECHNOLOGY;

MAY 1 6 1973

OATA AUTOMATiON DIVISION

2VCCESSING INDIVIDUAL

RECORDS FROM

personal data files

Using non-unique

identifiers

DO-2

NBS Special Publication 500-2

U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards
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mm

COMPUTER SCIENCE & TECHNOLOGY:

n

^ooi^ 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

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

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NBS Institute for Computer Sciences and Technology are designed to provide ADP

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computer utilization in the Federal sector, and to perform appropriate research and

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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 nonunique identifiers. (Computer science & technology) (NBS special publication ; 500-2) Supt.ofDocs.no.: C13. 10:500-2

I. Personnel records—Data processing. 2. Civil service—United

States—Personnel management. I. Moore, Gwendolyn B. H. 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 [JK766.5] 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

iii
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 coordinated this effort from its inception, gave incentive and direction

to the project while assisting in every 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 Kiariual Checks for Accuracy ^7

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

i

V
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 i

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

j The Privacy Act of 1974 places restrictions on the Federal, state, and

' local agencies' 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 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 ; retri eval

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 information technology, while essential to the efficient operations

of the Government, has greatly magnified the harm to individual privacy that can occur from any collection, maintenance, 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 information by such agencies. "1

There has been increasing public concern over the trend toward integration 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 information... In the course of conducting the study... the Commission 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,

since 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 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. "3

Privacy Act of 1974 , Public Law no. 93-579, Section 1.

Loc. cit.

Ibid. , Section 7.

2
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 individuals who may have lost, forgotten, or are unaware of their

identification numbers or symbols. For example, the combination of name, date of birth, or place of birth, and fatiier's

first name may be sufficient to identify an individual without the use of a system identification number.""^

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 "Guidelines for Implementing Section 3 of the Privacy Act of 1974," Federal

Register , 9 July 1975, Vol. 40, No. 32, pp. 28948-28978.
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 individual whose data is maintained in the files. The need to collect

such data in order to study causes of disease, poverty, violence, migration and other sociological phenomena is well understood, but the individuals 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 organizations, academic users, etc.; yet, the public is also concerned

about the increasing burden of providing the required statistics 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 effective exploitation of the data. This increases geometrically the

^ 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

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 distinction, 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 different. 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 individual

s

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 i not necessarily protected from unauthorized disclosure. Dr. Schltirer 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 -- perhaps -- 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 jnly

numbers of persons) is largely delusive, if the user is allowed to communicate with the data bank by dialogue.

A prerequisite for this intrusion by dialogue is preknowledge. 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 preknowledge must suffice to identify or, at least, nearly

identify Mr. X. An identification experiment with authentic

Martin, James, Secu'^ity, Accuracy, and Privacy in Computer Systems , Prentice-Hall, New Jersey, 1973, p. 432.

5
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 experiment. "7

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 j be made between the recorded characteristics and values in

j 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 po-ints up the feasibility of implementing 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

j Privacy has been perceptively defined by Westin as the right "...to

g

determine what information about ourselves we will share with others,"

Schl5rer, J., "Identification and Retrieval of Personal Records from a Statistical Data Bank," Methods of Information In Medicine , 14 (1, 1975).

'Fellegi, I. P., and A. B. Sunter, "A Theory for Record Linkage," Journal

of the American Statistical Association , (Dec. 1969), pp. 1183-1210.

Westin, Alan, as quoted by I. P. Fellegi, "On the Question of Statistical

Confidentiality," op. ci t. , 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 privacy 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 information from unauthorized 'others.' Such unauthorized access may be deliberate, 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 algorithm 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 questions about the content of the record before it is delivered to the re- questing individuals, and so forth. In the case of a computerized information 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 accidental 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 defined, 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. Problems 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 determination 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.

8
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 requirements. By definition the files all contain dossier-type data, but they

vary in terms of the non-biographical variables maintained, the ultimate 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 variables 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 maintaining such a system, the emphasis on manual and system checks of the variables 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, magazines, 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 introduction of new accessing requirements. Problems such as gross misspellings of names may block retrieval of certain records until the

level of accuracy of the NAME variable has been brought within reasonable 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 capabilities 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 problem.

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 particularly 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 granted. However, in actual data collection situations, surprising misunderstandings 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 biographical 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 personal reasons.

SEX: The incidence of encountering a patient with true

indeterminate sex status is very 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 obtain, 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 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.

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. "H

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 following 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 behavior 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."^^

The same tables show that, with respect to surnames onlyj20,000 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 description 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.

D. W. Fokker and M. F. Lynch, "Application of the Variety-Generator

Approach to Searches of Personal Names in Bibliographic Data BasesPart 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 soundalike names can be minimized, "-^^

In analyzing name lookup techniques, it is useful to refer to the information 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 degree 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 techniques 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.

Alpha Search Inquiry System General Infonnation Manual , GH20- 1188-3,

IBM Corporation, White Plains, N.Y., April 1974, p. 7. 13
including Soundexj the first widely used phonological ly based system for

name encoding. Most of the systems currently in use have been influenced 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

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). "14

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 together names that are somewhat different, as well as names that are similar; the same six codes would be obtained 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 lookup 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.

Knuth, Donald E., The Art of Computer Programming, Vol. Ill: Sorting

and Searching , Addison-Wesley, Reading, Mass., 1973, p. 392.

after the first:

b, f , p, V ^ 1 c, g,j,k, q, s,x, z-^2

d, t ^ 3

1 ^ 4

m, n ^ 5 r 6

15 Ibid.

14
Table 2.4-1 IBM Alpha Inquiry System Personal Name Encoding

Algorithm 1^

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, a zero is inserted in the first key

position and the basic table is used to code the first character into

the record key position.

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 character 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 The basic table used in this

this routine is: routine is:

Letter(s) Value Code Letter(s)

A 1 0 Z,S,CI,CY,CE,TS,TZ

E 1 1 D,T

GF 08 2 N GM 03 3 M

GN 02 4 R

H 2 5 L

I 1 6 J,SH,SCH,CH

J 3 7 C,G,K,Q,X,DG

KN 02 8 F,V,PH

0 1 9 B,P

PF 08

PN 02

PS 00

U 1 W (except WR) 4

WR 04

Y 5

^ ^Ipha Search Inquiry System General Information Manual , IBM Corporation, 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 0

CH 6 70 0

CK 7 7 6

C 7 7 6

K 7 7 6

DS 0 10 10

DZ 0 10 10

TS 0 10 10

TZ 0 10 10

Following are some examples of the results of coding personal names:

NAME H A R P E R KEY 2 4 9 4 = 24940000000000

NAME C 0 L L I E R KEY 07 5 4 = 07540000000000

NAME S C H U L T Z KEY 06 5 0 = 06500000000000

NAME L I V I N G S T 0 N KEY 05 8 2 7 0 1 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 missspellings (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 \j 1 / ~\j \j \j \j \j \j \j \j \j \j

ROGERS R262 04740000000000

Assign dissimilar

names to different logical group

KANT K530 02100000000000

KNUTH K530 07210000000000

In both these cases, the Alpha Search Inquiry System has solved the problem. 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 ROGERS with RGRS.

Thenumber 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 likely match for KNUTH, which points up the limitations of the system. It

is used successfully in this application because only a small file is

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. 1^ In this system, an attempt was

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
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 contiguous 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 characters.

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 [RCR]. 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.

Gail Hogan, op. ci t. 18
Table 2.4-3 Rules for Developing Phonetic Frequency Codes

NAME FIELD

J.KUHNS

G ALTSCHULER

CODE

1, In the name field, convert DK to K,

DT tn T Sr 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

? Rpmnvp vnwpl <^ W H and Y but keen

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

rndina

J.NS

G.LTSLR

13

5. Using the last letters of the name,

use Table PF3*to obtain the second

UIUIL Ui LMC L«L/VJC* 0.0 IMQIljr ICO

t^rs 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

Ft vc 1" n^mo RpmnuP fl'fl'PV^ mHinfl

NLTSL

157

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

o. me TTTtn digit is touna in tne same _ manner as the fourth using the re- maining characters of the name field,

if any. SL

16760

35797

*Defined 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 6.

Altshuler are reduced to the codes 16760 and 35797, respectively. However, 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 allocated 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 recording the name of 'Kuhns' may write 'Kuntz' instead. Also, persons

changing their name to a more angl icized 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 reliability for the New York State Criminal justice name inventory. However,

many of these rules are especially aimed at a large population of Spanish surnames and may at best not be useful for files with small populations 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 inspection. 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

npni IP

CODE VALUE ASSIGNED

n 1 0c

n4 brD 7 « Q

C rLr

r

A L D E

PFl

7L

1/l\ n

r DD u

LIn

Q u R I N T

V W

YA

S c F A 0 D M G U E

D C9 7L

1/l\ p RD R H N u uV

1

n YA I T1 w

B D F G M R S z

C T L J N Z E

K|-3 K P X H

Q I

V 0

U

W

YMN STN STR

TR SN SR

DRS PRS TN

TD

21
>-

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' 'PF' then change these letters to "FF' 'SCH' then change these letters to 'SSS'

2. If the last letters of the name are

'EE' then change these letters to 'Y^'

'IE' then change these letters to 'Y|5'

'DT' or 'RT' or 'RD' or 'NT' or 'ND' then change these letters to 'D^'

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 character of the name.

5. For each successive position of the pointer, only one of the following 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'.

b. If the current position is the letter

'Q' then change the letter to 'G'

'Z' 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 position 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' 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 inquiries 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 necessarily 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, nicknames, cross reference names such as aliases and maiden

names, etc.) against a file of 14 million records, processing 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. "^1

Personal Communication from Robert Keighley, Director of Systems and

Planninq, Recording and Statistical Division of Sperry Rand, the servicing agent for the MIB, October 14, 1975.

21

Ibid.

25
This example illustrates the feasibility of developing a highly successful retrieval system based on a name lookup technique. But the high degree of special treatment given to development of this system was a timeconsuming 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 effectively.

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, assuming that other non-unique attributes ca.n 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 bl ocks 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, ihe 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 subsequent 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

j common names the instances may stretch over several buckets.

j The system developed by the Immigration and Naturalization Service follows 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,

,1 gives flexibility to the retrieval system. The program is capable of

i 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

j 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

j 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 individual ly. "22b

i^Zapersonal Communication from Robert Robinson, Director of ADP Systems

Branch, Immigration and Naturalization Service, Department of Justice,

September 22, 1975.

22bLudwig, 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 letters of the last name and the birthdate. This information is reduced to two bytes through use of the following

algorithm:

3

Key = E (a+26^"'')+0-2400

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

beim 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 elioibility list, total donations to

date, indication of rare blood types, etc.

The system has been designed so that updating of some levels of information can be handled by reading in only the index level, and, when a donor record is required, it can be fetched directly.

A very 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 grouo 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 probability, 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

situatipns, particularly when the file size is large, the selection of

an efficient search strategy will be crucial to effective system performance. 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 together with their corresponding search algorithms, which are intended to

reduce the number of comparisons required to achieve the desired result. '"^'^ 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 key in the file closest to a given query key, and (2) find all kevs in the file

Keighley, op. ci t.

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 members of a file are keyed to a number of numerically-represented 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, subsets of tne file remaining to be examined are eliminated

without explicitly comparina the keys in these subsets

with the query key. Our experimental results (on randomlygenerated 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 message requesting that the user enter a set of key words taken

from various fields in the records; ...The system first generates 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 hybrid 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

^-" Ibid. , p. 236.

^^Abraham Bookstein, "A Hybrid Access Method for Bibliographic Records,"

Journal of Library Automation , 7:97-104, (2 June 1974), p. 99.

^^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 eliminate most records which would not pass the key word matching

test. It is stored in the index to the file along with the

address of the record. Though this Dreliminary test is not

foolproof, it could considerably reduce the size of the subfile 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 implementation 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

ij retrieval techniques might begin with Dennis Severance's generalized

J mode] for exploring the relationship between alternative search technl-

; ques^^ and Knuth's work on sorting and searching30.

29

I Dennis G. Severance, "Identifier Search Mechanisms: A Survey and

i Generalized Model," Computing Surveys , 6 (3 September 1974), pp. 175-194.

Knuth, op.ci t.

! 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 computerizing is to raise the accuracy of the files. "31

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 0 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

Wooldridge, Susan, Colin Corder, and Claude Johnson, Securi ty Standards

for Data Processing , John Wiley and Sons, New York, 1973, p. 60.

32Westin, A., Databanks in a Free Society .. Quadrangle Books , 1972^ p. 210.

•'''ibid.

Ibid , p. 433.

32
great extent because clues other than the data itself were readily available. 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- evaluated. 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 discussion, 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.

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

NON-UNIQUE IDENTIFIER

DECAY FACTOR

Does this variable remain constant

over time?

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

I

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 occurr-^d 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 obtaining such uniformity of data is discussed in the Uniform Hospital Discharge

Data Demonstration report. This project was directed toward improvement

in health care delivery 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 tnroughout 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. "^^

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. "^8

Hodgson, D. A., L. E. Kucken, and J. M. Ensign, The Uniform Hospital

Discharge Data Demonstration, Surrmary Report , Health Services Foundation, Chicago, Illinois, 1973, p. 24.

-^^ Ibid , p. 12.

^^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 consi dered. "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 collection 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 dialogue. 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.

37
FACTOR

easy

to

rately?

o

rENCY data accu

lements

CONSISl Is

the

record

YES

tics

Gf

Biographical

Data

E

RECOLLECTION

FACTOR

Is

the

data

easy

to

recall?

0

YES

1-1

Inherent

Characteris

RECOGNITION

FACTOR

Is

the

meaning

of

the

variable

easily

understood?

0z:

YES

ro

Form

NON-UNIQUE IDENTIFIER

MALE

FEMALE

UJ

ATUS

STICS

LAST NAME

FIRST

NAME

MIDDLE

NAM

SEX

BIRTHDATE

BIRTHPLACE

MARITAL

ST

ADDRESS

OCCUPATION EDUCATION PHYSICALCHARACTERI

38
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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 collection 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
Table 2 Percentage of Abstracts with

Discrepancies by Test Sites

ITEM 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 collection 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

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 anotlier paper on a similar subject, W. F. Kertisley 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). Consequently, 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:

^^Kemsley, W. F. , "Pre-Computer Editing of Budgets for the Family Expenditure Survey," Applied Statistics . Vol. 2, No. 1, 1972, pp. 58-64.

Ibid.

42
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"In some systems a11 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 contain 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 rea l time

as the operator makes them. The mistake or discrepancy is then

rectified on the spot."^'^

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 verification 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 encouraged 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

45Wooldridge, Susan, et al

.

, op.ci t. , p. 58.

47
ecks

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UJ Dd ZD LU

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BIRTHDAT BIRTHPLA MARITAL ADDRESS

OCCUPATI EDUCATIO PHYSICAL CHARACTE

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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 ."^^

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 'datamarking' , 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. "^^

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 likely to occur.

'Ibid. , p. 61.

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
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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 processed 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 manual checks of the source documents against listings of the records con- tained in the computer file. The 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 evaluation 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 between 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 section. 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 applications 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 'E'. 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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58
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 simulations 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, condition 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 between 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

foraiulations. 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 neglecting 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^, where the K^. each represent a particular value of a particular key field in the data base

records, and

2. If each of the fields in 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 product 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, although this may result in a decline in the recall ratio, thus causing

records to be missed which are actually contained in the data base. Equally, 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 probability 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..

and so this is the recall ratio.

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 identifying 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 increased 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 recordirjig 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 0

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 Retrieval 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 consideration the meaning of the keys. These values are used in the model described in Section 5.5.

63
Table 5.3-2 Statistical Classification of Surname Records as compiled

from Social Security Administration Accounts.

DECILE* Number of Records Retrieved'-^* % of File Retrieved**

n1 io. Jo . ooouoyo/A

o /I no 1

"7 4Uo. 1/ .0002459/6

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 on 1 value per surname key. Each decile contains 10%

of the population.

"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

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 involving the key fields for which these confidence factors have been

developed is simply the product of the confidence factors.

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 probability 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 problem 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 terfns

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.

From (2) and (3), we calculate

(4) cj = 1 - aj - bj.

Thus c. is the probability

66
P(Kj|M.)

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./li

Thus r. is the probability P(K.) given in Appendix II, p. 176.

(6) For each key value j we define

(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:

where n is the number of key values which match the request. The quantity 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

=

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 = L 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 j 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 0 (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 confidence 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 (recal 1 0.922) of Tables 5a - 5j in

Appendix L 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 improves 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 sych 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 implementation: 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 mathematical 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 dato 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 precision but rather low recall. This effect is obvious from consideration of the fact that the more identifiers used, the more complete the

identification of the individual becomes. At the same time, the introduction 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 legislation 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 Guidelines. Using the search keys that are both available and relevant,

weighting factors should be formed as outlined in Section 3. This procedure should be followed by an analysis of the error and omission rates

for these variables as suggested in Section 4. From these rates, confidence 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, however, that the Office of Management and Budget has stressed use of existing capabilities whenever possible.

"...the development of new retrieval and indexing capabilities 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.

Loc.Cit.

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 very 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 - Ij 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

Birthdate"Tyear/month/day) 164-173

73
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173
APPENDIX II

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 probability 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., is^ the record of the individual. This proposition

or event we designate as 'M. '; that is,

The second event to be specified in the probabilistic formulation is that

a record contains a particular attribute value for the jth retrieval key:

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

M. = a given record matches the individual i (1)

(e.g., who has attributes name = Xp address

= x^, attribute^ = x^)

Kj = a given record has a particular attribute

value for key j (e.g., name = x,

)

(2)

P(M. |Ki & K2 & . . . & & 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 studying the case of a single key, i.e., the probability

P(M.|Kj. & A) (4)

By the multiplication principle we have

P(M. |A) P(K, |M, & A)

P(M.|K. &A)= (5)

' J P(Kj|A)

For readability we will omit the reference event A, thus (5) assumes the

simpler form.

P(M.) P(K.1M.)

p(M |K ) = ^ ^—L- (6)

' J P(K.)

The factors on the right have the following meaning:

P(M. ) = the a priori probability that a (7)

' 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(Kj|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 " key is i's address, then P(K. |M. ) is the probability that i's record contains his correct address. It therefore

follows that

P(Kj|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 & ... SK^^ for Kj 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,& ... &K ) = P(M.) rr ^

, (9)

J = l

However, the independence assumption is only valid for certain keys. For

example, name and birthdate can be regarded as independent, but not birthdate and year of graduation from high school. Independence also depends

on the conditional probability. We therefore must look for a more general

solution.

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.54

We now present our version of the so-called "Johnson Cumulative Formula."

A set of rel evance coefficients or coefficients of influence is first

defined. For any propositions (events) . .

. , K , we define

P(KJK^ & A)

C(K,,KJA) = ^—^ (10)

^ ^ P(K^|A)

C(K^,K2 KJA) = C(K^, K2|A)-C(K^ & K3, KjA) (11)

It is then easy to show that C(Kj, K^|A) has the operator property*

P(K^ & ... & KjA) = C(K^, KjA) jf P(Kj|A) (12)

(see Carnap55).

^'^

The coefficient (11) is thus a coefficient of independence and is unity if

the events Kp K^, are independent relative to the event A.

The cumulative formula is obtained as follows:

Let K be the conjunction ... &K^. Then by the multiplication principle

P(M. |A) P(K|M. & A)

P(M.|k&a) = — (13)

' P(K|A)

Applying (12) to the numerator and denominator of (13) and then multiplying both sides of the result by [P(M. jA)]"-! 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.

^^ Ibid , p. 358, Formula T66-3C.

177
(14)

,1 C(K,, KJM. & A) n /p(M.. | A) P(K.iM. & A)

[P(M.|A)]"-^ • P(M.|K & A) = —i n \ ,

'

' ' C(K^, K A) j = l \ P(K.lA)

We now use the cumulative formula (14) in the following way. First introduce the abbreviations.

V = P(M. |K & A) (15)

P

= P(M. |A) (16)

qj = P(MilKj & A) (17)

By the multiplication principle, q. is the factor in the parentheses of

(14), i.e., J

P(M. |A) P(K.|M. & A)

q. = — ^

(18)

^ 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" = —^- ^—! TT q, (19)

C(K^, KJA) j = l 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 are then replaced

by their complements. We get.

n

1 C(Kt, K |%M. & A) n (1-p)"-^ (1-V) = ' \ TT (1 - q.) (20)

C(K^, KJA) j = l J

178
The ratio of (20) to (19) yields

where E is the factor

C(K,, K\-M. & A)

E = i ^ ^ (22)

C(K^, 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

-(Tr)""\rr(^^)

so that (21) becomes

from which we derive

T

- 1 = 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"^ 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 & Kp & ... & 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 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 recall 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 discuss 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 traditional 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 matched 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

Then let S(K. ) be the set of records which match on the i^" key. In

principle, the subset with maximum recall would be the set union of S(K^),

S(K^). 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
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. = a. + b.

1 1 1

(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^^ key. This quantity 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 ^m

(1) R. X R. X ... X R. is less than a certain threshold r . 'l '2 \

°

(2) e. X e. X ... X e. is less than a certain threshold e . h '2 \ °

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. = 0 otherwise.

1

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^- > 0 (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 . (1 - a.)

R.

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

" 1 R = 2 1- (1)

i=l

J. L. Thus, the score for the i key is

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

n

(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^. (1-a^. ) where i = 1, n, and so S can range from 0 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^. = 0 we should have a negative contribution which

183
varies as (1 - a.). We therefore take the score when f. = 0 to be

1 1 =-(l-a.)

Since a value of T^.= -1 fbr 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 .

)

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 IIIThe 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

.003 .00002 .5

^• .001 .01 0

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 0 .7442

Record 3 .9889 0 .9889

Record 4 .9900 0 .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
FORTRflH IV !>06-^ll SOURCE LISTIHG PRGE 001

C >»(5(<M")<;?(<*>«>K-»t>(o)<'t"»<*'t"<«*>tt*'.Jt< C QUERY SIMLtLATES THE Di^TA BASE/USER INTERhCTIOH

9081 DIhEHSTOH < 1 H . B ( 1 D , C H D , R C 1 1) , S ( 1 1

)

0002 DIMENSION JARV; 1 H , IFLD ( 11 > 0003 D I MENS I OH PiRGS (1 H

0004 DIMENSION SURHM( 10)

0005 COMfiuH IRAN, JRAN- EARY(2?; , PARV<3:i

0005 DATA ARCS <. 1 ) . 239726E- 2^, ARGS (2) / . 833333E- ARGS<35 / . 02/'

000? DATA ARGS (4) / . 02-^, PRGS (5) ^.5-^, ARGSJiG ) / . 600 1^', ARGS (7)

0006 DATA fiRGS ( 6) / . 66b6666/", ARGS (9) --'.000333333/, ARGS ( 10) / . 555555E-4-'

0009 DATA SUR'-IM(1)..SURHM(2) -SURNM(3) ,SURNMC4)/.9e7E-?, .2459E-5, .9S35E2 , .272037E--4/

00)0 DATA SURHM(5) ,SURNM(6) ,SURHM(;7)/4.81966E-5,6.627E -5.. 1.265E-4.''

00 1 1 DATA SURNMC8) , SURHM<9) , SURNMC t Q) /4 . 6 1 97E-4, 7 . S32E-4.. . 3384E-2/

0012 DATA , PARVf 1) ,PAPY<2) ,PARYC3)..-'4.545454E-9,2.5E-7, 1 .E-6/--

00 13 DATA EARY ( 1 ) , EAR Y< 2 ) , EAR Y ( 3 ) , EARY ( 4) . 5 , 1 . , 2 . , 1 0 , /

c

C QUERY SIMULATES QUERY PROCESSING AGAINST A DA I A BASE.

C IT GENERATES VALUES FOR A (J) L THE ERROR RATE IN A FIELD]

C B(J) [THE OMISSION RATE OF DATA IN A FIELD]

C C ( J ) C 1 - ( A

( J ) +B ( J ) ] . . THE COHF I DENCE FACTOR

C R(J) THE PERCENTAGE OF THE FILE RETRIEVED VIA A FIELD, AND

C Si; J) A QUERY SCORE, IJHICH RANGES FROM 1 TO 1"HE FILE SIZE.

C SU) IS NOMINALLY THE RATIO CCD/RCJ), AND IS ADJUSTED HERE

C TO WINTAIN REASONABLE LIMITS.

C THE USER ENTERS THE NUMBER OF ARGUMENTS (FROM 1 TO 11) C UHICH HE LUSHES TO USE IN HIS QUERY. THE QUERY IS TREftl"ED AS IF

C IT REQUIRED A SUCCESSFUL BOOLEAN AND OF ALL THE ARGUMENTS TO BE

C SUCCESSFUL.

C THE PROGRAM LULL TERMINATE LJHEN THE NUMBER OF INPUT ARGUMENTS

C IS SET TO ZERO.

C

C FOP THIS TEST THE VALUES OF A (J) AND B(.J) bJILL BE TREATED AS ZERO.

C FOLLOUING THE ENTRY OF THE FIRST NUMBER—THE NUMBER OF ARGUMENTS

C TO FOLLOLt--THE USER MAY ENTER NUMBERS CORRESPONDING TO THE ACTUAL

C ARGUMENTS. EACH ARGUMENT HAS AN ASSOCIATED RCJ), UHICH IS THE

C PERCENTAGE RETRIEVAL TO BE EXPECTED. THE ARGUMENT NUMBERS AMD

C THEIR ASSOCIATED VALUES ARE AS FOLLOUS:

C

C

c ARGUMENT NUMBER MEANING RETRIEVAL

c 1 DATE OF BIRTH <MMDD) 1/365

c 2 MONTH OF BIRTH 1/12

c

-3, YEAR OF BIRTH 1/50

c 4 STATE 1/50

c 5 SEX 1/2

c 6 STREET NUMBER 1/ 10, 000

c 7 STREET NAME 1/5, 000

c 8 EDUCATION 1/15

c 3 COUNTY 1/3000 (ESTIMATED)

L 10 DATE OF BIRTH (YMD) i/iS,8yo

RATIO

C BEGIN BY ASKING FOR OPriRATOR INPUT.

0014 1 JRITE(G,205)

C 5»o|'.'i<**'»"»5'|o|<>H>M:*5|o»^-HoK--H>)<>t< C NOW EXAMINE HARGS TO START THINGS GOING.

187
KORTRftN IV D0b-01 SOURCE LISTING PAGE 002

6015

66 ib

66 J ?' 68 1

8

6019

8028

0821

0022

0023

QQ24

0025

0026

0027

0628

0629

0030

607 1 6032

0633

6634

0035

6836

003?

6038

0039

8049

0041

0642

6043

8044

0645

6046

604?

0048

0049

6050

6051

6052

0053

8054

6055

085f

605?

0058

0059

0060

8061

0062

0063

8064

6055

0066

REAP (6.216) HrRGS

iF- fHHKGS) 100. 100.

c lcme here to prepare H

10

QUERY SURROGhTE ,

10 I = i NhRGS

(6, 220)

(6.210) J

:i

32

33

34

35

3 b

J,B(J) , J,C(J) , J,R(Ji - J,S(J) , J.JnRVtJ)

-R(NARGS-i-l)

DO 26

UR i TE

READ

A ( I ) =0 . B ( I )

.

C ( I) = 1 . R(I)=hRGS(J)

S( I) =''C( I)/R( I) ) JARY<I)=.J

COHTIriUE

bJRITt (6.. 2 15)

C HOU CALL ON THE MAIN COMPUTATIOHAL ROUTINE TO PREPARE THE TABLES.

r;^;1'-;K.-+<-4<>)<-+:>t<?i<>KH«^=1<>H;h:?t<>f<>f"|5* JARY(HARG3+1) =1

i

N=HARC^+i

yRITE(5,200) NhRGS

DO 25 J=l,. HARGS

URITE(5,245) J. A (J)

25 COHTIi-iUE

DO 56 I = i. 10

C (NARGS+i = 1 . B (NARGS+i ) =0

.

A (HHRG3+1 ) '-3 .. R<NARGS+1> =SURNM( I) S(NARGS+1) =C(NARGS (-1). URITE(5,250)

DO 45 .J=1..NARGS

GOTO <31..32..33.34..35,3b,3?.

UR^TE(5,255)

GO I 0 4b

WRITE (5- 260)

GOTO 45

UR^TE(5,.265)

GO I 0 4b

t.JPITE(5,2?e)

GOTO ^45 LJRITE(5,2?5)

GOTO 45

LiR ITE(5 .280)

GOTO 45

UR RE (5.. 285)

GOTO 45

UP I TE (5.. 290)

GOTO 45

39 UJR_I_TE(5,295)

GU!0 45

40 LJRITE(5,3e0)

45 CGHTIHUE

WRITERS, 305)

yR!TE(5, 150)

URITE (5,225) I yRI-E'5.23G)

30,39,40^ JARYtJJ

188
FORTRCIN IV D0b-01 SOURCE LISTING- PAGE 003

6068

0069

00?0

00? 1 00?2

00?3

00|-'4 150

200

00?6 2 0 'J 00?? 2 i '3 00 ?l.i 2 1 b

0 0 ? 9 220

0 0 y 0 003 1 230

u y ti

^ 2 J 'J

00S3 246

0064 245

0035 250

0086 255

008? 26?

0088 265

0089 2?0

0036 2r5

009 i 260

0092

0393

285

290

0094 2'='5 0095 300

0096 365

009?

13X, 1 10('-'

)

'HhRG3 = '

.. 12.) ENTER NUMBER

iJRITE':5,235:j

I I h i;5, 240)

CALL pycY';H,e,c, R.. 30 CONTINUE

GO TO i

1 1? 0 C »Ll. P VC V ( H , B , C , R .. CALL EXIT

C FORMAT STATEMENTS - FORMAT

FORMAT

FORMAT

FORMAT

FORMAT

FORMAT r

FORMAT' i

I

' SURNAME

FORMAT f, IH

1 IH , i3X.. 2 2X^ ! ' 23X

FuRr:AT( i H . 13X, '

! '

..

1 If! , 13X, ! , 15X, '! 2 ' 4 MILL ION'' .. 8X, '

!

'" FORMAT'.: IH , 13K, '

!

" FORMAT': 1H0 ' A (' . 12, '

1 3X. 'C( - , 12, ' ) =' ,E15

N, JARY)

B,NARGS, JhrY)

?X>

OF ARGUMENTS

IH ,

i H i

,

(

' 0

(12)

!•; ' EXITING QUERY' ) r ENTER QUERY FIELD IDENTIFIER')

13X. • r , leeX, '

! '.?K/1H ,13X,

. 43X,

DEUlLt ' , IX, 12,48,><,, '

! '

, 13X, '! , 108('-' ) ,

'

! '

! ' , 3X, 'ERROR RATE ' , 2X

ILE SIZE' ,3?X, '

!

, '! ', ibX, 'i ' i?X,

'^1

, 5X,

108 (

?X

1

dX, ' RhCALL ^ ACTOR'

,

, 10X,

220 MILLIUH'

E15.8,3X, 'B(

i;X, 'RC , 12, ' >

f4( ' -'

MILLION

bX. '

!

'XX

t

2 12, ) = ' .E15.8,3X, JARYC , 12.- ') = ', 12)

FORMAT a H 1 , 43X, ' RREC I S ION OF RETR lEVED

1 ,1-1 ELDS')

FORMATC IH FORMAT ( IH FORMAT'; IH FORMA^;^ IH

I- UK MA ! ( IH FORMATC IH FORMAT ( IH FORMAT'; iH

FORMATC IH FORMiATC IH FORMATC IH END

, 48 X.

, 46 X

, 48X.

, 48X. ,48X^

, 48 X.

, 48 X.

, 48X.

, 48X,

, 4SX.

, 48 X

12, '

) =' ,Ei5.8,

,E15.8,3X., 'SC

RESULTS FOR DATA

'DATE OF_BIRTH (MONTH AND DAY ONLY) ) 'MONTH OF BIRTH'

)

' YEAR OF B IRTH'

)

'STATE')

STREET NUMBER'

STREET NAME'

)

EDUCATION'

)

' COUNTY'

)

'DATE OF BIRTH

'AND SURNAME')

(YtHR, MOH'ri, AND DAY)'

189
STORAGE MRP

H

ElCR

SJARY

IFLD

HRGS

SURHM

HARGS

IJ

NPVCY

EXIT

606006

005062

6130212

I3002G6

9063.42

0005P0

600416

000472

002100

002102

602104

002 106

000000

000000

COMMGN BLOCK //

IRAN

JRAN

EARV

PAR't'

000000

000002

808004

000160

REAL*4

REAL.-K4

REAL*4

REAL.--h:4 REAL*4

1NTEGE:R'.k2 IHTEGER*2

REAL +4

REAL?K.4 IHTEGER't<2

INTEGER*2

Ih rEGER=i-2

il-iTEGER>i<2 REAL>f:4 RErtL*4

ARRAY

ARRAY

ARRAY

ARRAY

ARRAY

ARRAY

ARRAY

ARRAY

ARRAY

VARIABLE

VARIABLE

VARIABLE

VARIABLE

PROCEDURE

PROCEDURE

(11)

(11)

( 1 1) (11)

( 1 1 )

( 1 1

( 1 1

( 1 1 (10

LtNGTH 000174

IHTEGERf^S

IHTEGER*2

REhL:-k-4 REAL-+:4

VARIABLE

VARIABLE

ARRAY (27)

ARRAY (3)

190
FORTRAN r D06-01 SOURCE LISTING PhGE 001

Sua i

6002

f=)B03 0004

13065 0006

000?

0008

''1009 06 10 06 1 i 00 12 00 1 3 00 14

0015

00 15 0M t ? 0016

09 19

0020

003 i 0022

0623

602';! Q025

W L1 2 r3 '''32? 0029

0030

003 1 0032

9033

bU34

0035

PRIVACY .MCDEL--MARi::H 19?6

BEEN MODIFIED TO PRODUCE H

C.^'.>t'^+^?K^K>K'1<'^<.H:>^^^^?K^^<'t'**>^>^<>K?K C liHlTl COMPUTATION ROUTINE OF

C ^»<|--iOTe: THIS SUBROUTINE HAS

(J:1<j|<* KJi<>*<H<'lc-trH;>t;>h;<<->K>|<>K>t'5t< SubKUiJTIiiE PvT:y( A J . BJ, CJ, RJ, SJ, NARGS, JARY)

C ^~>KH^K*>l<^'>t•M<*.'t ?h-i<.'K-+;K*;(<>l<.+: C DECLARE VARIABLES

C;t^^^<;K-+-*H^5t^;^•>KH<:H"l*>^:l^>l<>K>^<>K>^< D IMEHS luhi jhRy( 1 i

)

DOUBLE PRECISION B COMMON

REAL A

TABULAR PRINTOUT.

:GPH 1 n .F.VARYn3,3J

I RAN , JRAH .. EAR Y( 27 ') , PARV ( 3

)

J i 1 1 ) , BJ < 1 1 .) , C J (

1 1) , RJ (11) , S J (in

KtP'. uARi'(ir)

DATA VARY ( 1 , 1 )

. ''9 . ODO--'

C MAIN LOOP STARTS HERE

CC VALUES ARE RETURNED IN THE ARGUMENT ARRAY:

IF (NARGS) 95.. 95^ 1 1

C BEGIN PROCESSING THE QUERY RESPONSE HERE

1 1 CONTINUE

DO 90 JK = 1,13

L=.TK CONF = 1

. -0 . 005* ( L- 1

R E F A C = C 0 N r ( N A R G S

)

ERRAT= 1

. -CONF

PERERR=ERRAT>t^i00

DO 80 I

=^ i , 3

P=PhRY( I) DO 20 II = i , HARGS

20 QARY ( I T ) =pJt<S J ( I I ) *CONF

C-^***^'**H^-'+^?^:^->t"t<?i<:^^'.io|'>t<>K C NO(..J COMPUTE THE FUNCTION F

F=l ., K = P.^f 1 . -P)

DO 30 J.J=1 -NARGS

B I GP I ( J J ) - ( 1 . - Q A R Y ( J J ) ) .^3A R Y ( J J

)

30 F«F*X*B IGPI ( JJ)

F=F..-<K

C PREPARE THE PFIHTOUT TA^LE

VARY ( Ji< .. I'l = 1 . .' ( 1 . +F

)

.GT

-TERMINATE IF NARGSIF(VARY(.JK, t; CONTINUE

LIRITE';5, 150)

CON I iNUE

1 ? 0

)

1.0) ) VARY(JK:, D-l .0

PERERR,REFAC, ^VARYcJK, V) , i

- 1,-1)

IJRITEC; RETURN

C E X I r V J A HERE UHEN W I- :R G S = 0

95 bJR I TE (5, 200)

RETURN

191
FGR"^RfiN IV 1)06-01 SOURCE L I STING PfiGE 002

C F 'MAT SThTEMEHT SECTION

r" ^^;^l'.-^<;^^,^:^•.;^^^Cilc.!^^{^f^;^K;|^?K^K^<^K>l< ei33G 156 FORMAT <1H , 13X, '

' \' , 17X, * ! ' , 23X- ' 1' , 2?X, ' f',22K,

1 IH .. 13X,. ' r ..4X,FS.2, ,4X,. '! ' ,4X,FS.4,5X, =

! %4X..

2 F i5. 8,4>^, 'i %6X.E15.8,6X, 'I ' , 3X.. E 15 . 3,4X, ' ! ' ,7X)

0037 170 PORMATCIH , i3X, ilQC "'),7X)

eB'^S Z-tm FOPhMTdHG,' HORMRL TERMIHRTIOH'

)

003:? EhiD

192
FORTRfl!^ IV STORflGE MhP

IHTEGFRf<2 PhRhMETER ARRAY (11)

B IGP I REi-lL--i;8 flRRHN' <11)

VhRy f?i!3C' 162 REHL''Cd ARRAY (13- 5,1 VElTOReI;

A,J REAL*4 PARAMETER ARRAY (11)

B.J 6660 lb REHL-i^4 PARAhETER ARRAY ( 1

1

)

CJ 666^26 REHL>h:4 PARAMETER ARRAY (11)

R J QQtiy22 REHL+-4 PHKHllETbR ARRAY (11)

G.T Pl;'6e24 RErtL*4 PARAMETER ARRAY ( 1 1

)

QhRV 01^0652 REhL*4 ARRA'r (11)

T L 1 "T* i~' f" lTMi .1^ —1 I!iTciiER*.2 PmPH'IETER VriR IhBLE

F 60 I i 46 REAL-N8 'v'AR I At'iLE P 06 1 i 50 R£hL>k4 VhR IhBLE

JK 00 1 154 1 N 1 Ebl.;R?)<2 vhR iHbLE

L 06 1 i5b INTEGER ••(^2 VARIABLE

CONF 661160 REhL^4 VARIAeLE

REFAC i^Sl 164 REAL>k4 VARIABLE

E^RRhT 061 i?6 REf-iLn<4 VARIAPi.,E /

r"i r~" r*-! r~ r~i i—i perekr 00 i 1 74 REhL>k4 VARIABLE

I 00 1 200 IHTEGERK2 VARIABLE

I I 06 1 202 IHTEGER'tsZ' VARIABLE

X 06 1264 rehlm VARIABLE

J J 001210 iNTEGERt<2 VARIABLE

CUfiMfiN tlL:Ji:K ..-v LENGTH 000174

trhh 000060 INTEGER'^2 VARIRPLE

jrhh 000662 IHTEGERi^2 VARIABLE

Ef'iRY Pian064 RE!^L*4 ARRAY (2?)

PfiRY 000 160 REi-iL>i':4 ARRAY (3)

193

II3S-114A (REV. 7-73)

U.S. DEPT. OF COMM. 1. PUBLICATION OR RKPORT NO. 2. Gov't Accession BIBLIOGRAPHIC DATA No. SHEET NBS SP-500-2

3. Recipient's Accession No.

TITLE AND SUBTITLE

1Arrpssino \ \^ \^ \^ ^ tj IIIVJ Individual XII \tl 1 V 1 u U \A I Records 1 ^ V« w W 1 VJ'-J From 1 1 will Personal I ^1 \J I M 1 Data Files Using Non-Unique Identifiers

5. Publication Date

jrcL/xu-dxy J-7< 1 6. Performing Organization Code

AUTHOR(s) Gwendolyn B. Moore, John L. Kuhns,

ileffrey L. Trefftzs, Christine. A. Montgorrery

8. Performing Organ. Report No.

1,

2

PERFORMING ORG AI^IZATIGN NAME ANfD ADDRESS

uperanng oystems, inc.

21031 Ventura Boulevard

Woodland Hills, California 91364

10. Project/Task/Work Unit No. 640.1118

11. Contract/Grant No. 5-35928

. 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

5. SUPPLEMENTARY NOTES

' Library of Congress Catalog Card Number: 76-57950

!i6. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here.)

I The Privacy Act of 1974 places restrictions on the Federal, state and local agencies'

i 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

j validation techniques for the error and omission rates of data items are suggested,

and a retrieval probability model--desi gned to show likelihood of retrieval of a

I subject's record given a variety of populations, combinations of identifiers, and

error/omission rates--is described. A methodology is developed for forming confidence factors from the established error/omission rates for combinations of nonunique identifiers that are candidates for use as retrieval keys. Use of these

I confidence factors as indices into the precision tables produced by the probability

I model is described.

7. 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.

8, AVAILABILITY [X] Unlimited

1 ! For Official Distribution. Do Not Release to NTIS

C^Order From Sup. of Doc, U.S. Government Pj-LntUiS\ftffife Washington, D.C. 20402. SD Cat. No. C13 • iU:5vU-Z

1 ! Order From National Technical Information Service (NTIS)

Springfield, Virginia 22151

19. SECURITY CLASS

(THIS REPORT)

UNCL ASSIFIED

21. NO. OF PAGES

2 03

20. SECURITY CLASS

(THIS PAGE)

UNCLASSIFIED

22. Price $2.65

USCOMM-DC 29042-P74

jU.S. GOVERNMENT PRINTING OFFICE: 1977 Z40-848/120 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' 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 OfRce 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 " — Electromagnetics - — Time and Frequency

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 Evaluation 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 — Information 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.

' Headquarters and Laboratories at Gaithersburs;, Maryland, unless otherwise noted: mailing address Washington, D C. 20234.

- Located at Boulder. Colorado 80302.
U.S. DEPARTMENT OF COMMERCE

National Bureau of Standards

Washington. D.C. 20234

OFFICIAL BUSINESS

Penalty for Private Use, $300

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