简介
Summary:
Publisher Summary 1
Statistical disclosure control is the discipline that deals with producing confidential statistical data that are safe enough to be released to external researchers. The book will be useful for official, social, and medical statisticians and others who are involved in releasing personal or business data for statistical use.
Publisher Summary 2
Statistical disclosure control is the discipline that deals with producing statistical data that are safe enough to be released to external researchers. This book concentrates on the methodology of the area. It deals with both microdata (individual data) and tabular (aggregated) data. The book attempts to develop the theory from what can be called the paradigm of statistical confidentiality: to modify unsafe data in such a way that safe (enough) data emerge, with minimum information loss. This book discusses what safe data, are, how information loss can be measured, and how to modify the data in a (near) optimal way. Once it has been decided how to measure safety and information loss, the production of safe data from unsafe data is often a matter of solving an optimization problem. Several such problems are discussed in the book, and most of them turn out to be hard problems that can be solved only approximately. The authors present new results that have not been published before. The book is not a description of an area that is closed, but, on the contrary, one that still has many spots awaiting to be more fully explored. Some of these are indicated in the book. The book will be useful for official, social and medical statisticians and others who are involved in releasing personal or business data for statistical use. Operations researchers may be interested in the optimization problems involved, particularly for the challenges they present. Leon Willenborg has worked at the Department of Statistical Methods at Statistics Netherlands since 1983, first as a researcher and since 1989 as a senior researcher. Since 1989 his main field of research and consultancy has been statistical disclosure control. From 1996-1998 he was the project coordinator of the EU co-funded SDC project.
目录
Table Of Contents:
Preface v
Overview of the Area 1(38)
Introduction 1(3)
Types of Variables 4(9)
Categorical variable 4(2)
Hierarchical variable 6(1)
Continuous/Numerical/Quantitative Variable 6(1)
Identifying Variable 7(2)
Sensitive Variable 9(1)
Weight Variable 9(1)
Regional Variable 10(1)
Household Variable 11(1)
Spanning Variable and Response Variable 12(1)
Shadow Variable 12(1)
Types of Microdata 13(1)
Simple Microdata 13(1)
Complex Microdata 14(1)
Types of Tabular Data 14(5)
Single Tables 15(1)
Marginal Tables 15(2)
Hierarchical Tables 17(1)
Linked Tables 17(1)
Semi-linked Tables 17(1)
Complex Tables 18(1)
Tables from Hierarchical Microdata 19(1)
Introduction to SDC for Microdata and Tables 19(3)
Intruders and Disclosure Scenarios 22(1)
Information Loss 23(3)
Information Loss for Microdata 25(1)
Information Loss for Tables 25(1)
Disclosure Protection Techniques for Microdata 26(7)
Local Recoding 26(1)
Global Recoding 27(1)
Local Suppression 28(1)
Local Suppression with Imputation 29(1)
Synthetic Microdata and Multiple Imputation 29(1)
Subsampling 29(1)
Adding Noise 30(1)
Rounding 30(1)
Microaggregation 30(2)
PRAM 32(1)
Data Swapping 32(1)
Disclosure Protection Techniques for Tables 33(6)
Table Redesign 33(1)
Cell Suppression 33(2)
Adding Noise 35(1)
Rounding 36(1)
Source Data Perturbation 36(3)
Disclosure Risks for Microdata 39(32)
Introduction 39(1)
Microdata 40(1)
Disclosure Scenario 40(2)
Predictive Disclosure 42(4)
Re-identification Risk 46(6)
Risk Per Record and Overall Risk 52(1)
Population Uniqueness and Unsafe Combinations 53(1)
Modeling Risks with Discrete Key Variables 54(7)
Direct Approach 55(2)
Model Based Approach 57(4)
Disclosure Scenarios in Practice 61(3)
Researcher Scenario 62(1)
Hacker Scenario 63(1)
Combinations to Check 64(4)
A Priori Specified Combinations 64(2)
Data Driven Combinations: Fingerprinting 66(2)
Practical Safety Criteria for Perturbative Techniques 68(3)
Data Analytic Impact of SDC Techniques on Microdata 71(22)
Introduction 71(3)
The Variance Impact of SDC Procedures 74(1)
The Bias Impact of SDC Procedures 75(1)
Impact of SDC Procedures on Methods of Estimation 75(1)
Information Loss Measures Based on Entropy 76(8)
Local Recording 77(1)
Local Suppression 78(1)
Global Recoding 78(1)
PRAM 79(1)
Data Swapping 79(1)
Adding Noise 80(1)
Rounding 80(1)
Microaggregation 81(3)
Alternative Information Loss Measures 84(5)
Subjective Measures for Non-perturbative SDC Techniques 85(1)
Subjective Measures for Perturbative SDC Techniques 86(1)
Flow Measure for PRAM 87(2)
MSP for Microdata 89(4)
Application of Non-Perturbative SDC Techniques for Microdata 93(14)
Introduction 93(1)
Local Suppression 94(8)
MINUCs Introduced 94(1)
Minimizing the Number of Local Suppressions 95(3)
Minimizing the Number of Different Suppressed Categories 98(1)
Extended Local Suppression Models 99(2)
MINUCs and μ-ARGUS 101(1)
Global Recoding 102(4)
Free Global Recoding 103(2)
Precoded Global Recoding 105(1)
Global Recoding and Local Suppression Combined 106(1)
Application of Perturbative SDC Techniques for Micro-data 107(30)
Introduction 107(1)
Overview 107(1)
Adding Noise 108(2)
Rounding 110(5)
Univariate Deterministic Rounding 110(2)
Univariate Stochastic Rounding 112(1)
Multivariate Rounding 113(2)
Derivation of PRAM Matrices 115(11)
Preparations 116(2)
Model I: A Two-step Model 118(2)
Model II: A One-step Model 120(3)
Two-stage PRAM 123(2)
Construction of PRAM Matrices 125(1)
Some Comments on PRAM 126(1)
Data Swapping 126(2)
Adjustment Weights 128(9)
Disclosing Poststrata 128(2)
Disclosure for Multiplicative Weighting 130(4)
Disclosure Control for Poststrata 134(3)
Disclosure Risk for Tabular Data 137(22)
Introduction 137(1)
Disclosure Risk for Tables of Magnitude Tables 138(8)
Linear Sensitivity Measures 140(1)
Dominance Rule 141(1)
Prior-posterior Rule 141(2)
Intruder's Knowledge of the Sensitivity Criterion Used 143(1)
Magnitude Tables from a Sample 144(2)
Disclosure Risk for Frequency Count Tables 146(4)
Frequency Count Tables Based on a Complete Enumeration 147(2)
Frequency Count Tables Based on Sample Data 149(1)
Linked Tables 150(2)
Protection Intervals for Sensitive Cells 152(5)
Sensitivity Rules for General Tables 157(2)
Information Loss in Tabular Data 159(16)
Introduction 159(2)
Information Loss Based on Cell Weights 161(4)
Secondary Cell Suppression 161(3)
Rounding 164(1)
Table Redesign 164(1)
MSP for Tables 165(2)
Table Redesign 165(1)
Secondary Cell Suppression 166(1)
Rounding 167(1)
Entropy Considerations 167(8)
Some General Remarks 168(1)
Tabulation 169(1)
Cell Suppression 170(1)
Table Redesign 171(2)
Rounding 173(2)
Application of Non-Perturbative Techniques for Tabular Data 175(44)
Introduction 175(1)
Table Redesign 176(1)
Cell Suppression 177(2)
Some Additional Cell Suppression Terminology 179(5)
The Zero-Extended Table 179(2)
Paths, Cycles and Their Cells 181(2)
Network Formulation for Two-dimensional Tables 183(1)
Hypercube Method 184(4)
Secondary Suppression as an LP-Problem 188(2)
The Underlying Idea 188(2)
Secondary Suppression as a MIP 190(14)
Lougee-Heimer's Model 191(1)
Kelly's Model 192(3)
Geurts' Model 195(2)
Fischeti and Salazar's Model 197(6)
Partial Cell Suppression 203(1)
Cell Suppression in Linked Tables 204(4)
Top-Down Approach 204(2)
Approach Based on MIP 206(2)
Cell Suppression in General Two-Dimensional Tables 208(4)
Cell Suppression in General Three-Dimensional Tables 212(4)
Comments on Cell Suppression 216(3)
Application of Perturbative Techniques for Tabular Data 219(26)
Introduction 219(1)
Adding Noise 220(1)
Unrestricted Rounding 221(4)
Deterministic Rounding 221(3)
Stochastic Rounding 224(1)
Controlled Rounding 225(7)
Controlled Rounding in One-Dimensional Tables 226(1)
Controlled Rounding in Two-dimensional Tables 227(5)
Controlled Rounding by Means of Simulated Annealing 232(3)
Simulated Annealing 232(2)
Applying Simulated Annealing to the Controlled Rounding Problem 234(1)
Controlled Rounding as a MIP 235(4)
The Controlled Rounding Problem for Two-dimensional Tables 237(1)
The Controlled Rounding Problem for Three-dimensional Tables 238(1)
Linked Tables 239(6)
Rounding in Linked Tables 239(1)
Source Data Perturbation 240(5)
References 245
Preface v
Overview of the Area 1(38)
Introduction 1(3)
Types of Variables 4(9)
Categorical variable 4(2)
Hierarchical variable 6(1)
Continuous/Numerical/Quantitative Variable 6(1)
Identifying Variable 7(2)
Sensitive Variable 9(1)
Weight Variable 9(1)
Regional Variable 10(1)
Household Variable 11(1)
Spanning Variable and Response Variable 12(1)
Shadow Variable 12(1)
Types of Microdata 13(1)
Simple Microdata 13(1)
Complex Microdata 14(1)
Types of Tabular Data 14(5)
Single Tables 15(1)
Marginal Tables 15(2)
Hierarchical Tables 17(1)
Linked Tables 17(1)
Semi-linked Tables 17(1)
Complex Tables 18(1)
Tables from Hierarchical Microdata 19(1)
Introduction to SDC for Microdata and Tables 19(3)
Intruders and Disclosure Scenarios 22(1)
Information Loss 23(3)
Information Loss for Microdata 25(1)
Information Loss for Tables 25(1)
Disclosure Protection Techniques for Microdata 26(7)
Local Recoding 26(1)
Global Recoding 27(1)
Local Suppression 28(1)
Local Suppression with Imputation 29(1)
Synthetic Microdata and Multiple Imputation 29(1)
Subsampling 29(1)
Adding Noise 30(1)
Rounding 30(1)
Microaggregation 30(2)
PRAM 32(1)
Data Swapping 32(1)
Disclosure Protection Techniques for Tables 33(6)
Table Redesign 33(1)
Cell Suppression 33(2)
Adding Noise 35(1)
Rounding 36(1)
Source Data Perturbation 36(3)
Disclosure Risks for Microdata 39(32)
Introduction 39(1)
Microdata 40(1)
Disclosure Scenario 40(2)
Predictive Disclosure 42(4)
Re-identification Risk 46(6)
Risk Per Record and Overall Risk 52(1)
Population Uniqueness and Unsafe Combinations 53(1)
Modeling Risks with Discrete Key Variables 54(7)
Direct Approach 55(2)
Model Based Approach 57(4)
Disclosure Scenarios in Practice 61(3)
Researcher Scenario 62(1)
Hacker Scenario 63(1)
Combinations to Check 64(4)
A Priori Specified Combinations 64(2)
Data Driven Combinations: Fingerprinting 66(2)
Practical Safety Criteria for Perturbative Techniques 68(3)
Data Analytic Impact of SDC Techniques on Microdata 71(22)
Introduction 71(3)
The Variance Impact of SDC Procedures 74(1)
The Bias Impact of SDC Procedures 75(1)
Impact of SDC Procedures on Methods of Estimation 75(1)
Information Loss Measures Based on Entropy 76(8)
Local Recording 77(1)
Local Suppression 78(1)
Global Recoding 78(1)
PRAM 79(1)
Data Swapping 79(1)
Adding Noise 80(1)
Rounding 80(1)
Microaggregation 81(3)
Alternative Information Loss Measures 84(5)
Subjective Measures for Non-perturbative SDC Techniques 85(1)
Subjective Measures for Perturbative SDC Techniques 86(1)
Flow Measure for PRAM 87(2)
MSP for Microdata 89(4)
Application of Non-Perturbative SDC Techniques for Microdata 93(14)
Introduction 93(1)
Local Suppression 94(8)
MINUCs Introduced 94(1)
Minimizing the Number of Local Suppressions 95(3)
Minimizing the Number of Different Suppressed Categories 98(1)
Extended Local Suppression Models 99(2)
MINUCs and μ-ARGUS 101(1)
Global Recoding 102(4)
Free Global Recoding 103(2)
Precoded Global Recoding 105(1)
Global Recoding and Local Suppression Combined 106(1)
Application of Perturbative SDC Techniques for Micro-data 107(30)
Introduction 107(1)
Overview 107(1)
Adding Noise 108(2)
Rounding 110(5)
Univariate Deterministic Rounding 110(2)
Univariate Stochastic Rounding 112(1)
Multivariate Rounding 113(2)
Derivation of PRAM Matrices 115(11)
Preparations 116(2)
Model I: A Two-step Model 118(2)
Model II: A One-step Model 120(3)
Two-stage PRAM 123(2)
Construction of PRAM Matrices 125(1)
Some Comments on PRAM 126(1)
Data Swapping 126(2)
Adjustment Weights 128(9)
Disclosing Poststrata 128(2)
Disclosure for Multiplicative Weighting 130(4)
Disclosure Control for Poststrata 134(3)
Disclosure Risk for Tabular Data 137(22)
Introduction 137(1)
Disclosure Risk for Tables of Magnitude Tables 138(8)
Linear Sensitivity Measures 140(1)
Dominance Rule 141(1)
Prior-posterior Rule 141(2)
Intruder's Knowledge of the Sensitivity Criterion Used 143(1)
Magnitude Tables from a Sample 144(2)
Disclosure Risk for Frequency Count Tables 146(4)
Frequency Count Tables Based on a Complete Enumeration 147(2)
Frequency Count Tables Based on Sample Data 149(1)
Linked Tables 150(2)
Protection Intervals for Sensitive Cells 152(5)
Sensitivity Rules for General Tables 157(2)
Information Loss in Tabular Data 159(16)
Introduction 159(2)
Information Loss Based on Cell Weights 161(4)
Secondary Cell Suppression 161(3)
Rounding 164(1)
Table Redesign 164(1)
MSP for Tables 165(2)
Table Redesign 165(1)
Secondary Cell Suppression 166(1)
Rounding 167(1)
Entropy Considerations 167(8)
Some General Remarks 168(1)
Tabulation 169(1)
Cell Suppression 170(1)
Table Redesign 171(2)
Rounding 173(2)
Application of Non-Perturbative Techniques for Tabular Data 175(44)
Introduction 175(1)
Table Redesign 176(1)
Cell Suppression 177(2)
Some Additional Cell Suppression Terminology 179(5)
The Zero-Extended Table 179(2)
Paths, Cycles and Their Cells 181(2)
Network Formulation for Two-dimensional Tables 183(1)
Hypercube Method 184(4)
Secondary Suppression as an LP-Problem 188(2)
The Underlying Idea 188(2)
Secondary Suppression as a MIP 190(14)
Lougee-Heimer's Model 191(1)
Kelly's Model 192(3)
Geurts' Model 195(2)
Fischeti and Salazar's Model 197(6)
Partial Cell Suppression 203(1)
Cell Suppression in Linked Tables 204(4)
Top-Down Approach 204(2)
Approach Based on MIP 206(2)
Cell Suppression in General Two-Dimensional Tables 208(4)
Cell Suppression in General Three-Dimensional Tables 212(4)
Comments on Cell Suppression 216(3)
Application of Perturbative Techniques for Tabular Data 219(26)
Introduction 219(1)
Adding Noise 220(1)
Unrestricted Rounding 221(4)
Deterministic Rounding 221(3)
Stochastic Rounding 224(1)
Controlled Rounding 225(7)
Controlled Rounding in One-Dimensional Tables 226(1)
Controlled Rounding in Two-dimensional Tables 227(5)
Controlled Rounding by Means of Simulated Annealing 232(3)
Simulated Annealing 232(2)
Applying Simulated Annealing to the Controlled Rounding Problem 234(1)
Controlled Rounding as a MIP 235(4)
The Controlled Rounding Problem for Two-dimensional Tables 237(1)
The Controlled Rounding Problem for Three-dimensional Tables 238(1)
Linked Tables 239(6)
Rounding in Linked Tables 239(1)
Source Data Perturbation 240(5)
References 245
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