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Inhaltsangabe1 Overview of the Area.- 1.1 Introduction.- 1.2 Types of Variables.- 1.2.1 Categorical variable.- 1.2.2 Hierarchical variable.- 1.2.3 Continuous/Numerical/Quantitative Variable.- 1.2.4 Identifying Variable.- 1.2.5 Sensitive Variable.- 1.2.6 Weight Variable.- 1.2.7 Regional Variable.- 1.2.8 Household Variable.- 1.2.9 Spanning Variable and Response Variable.- 1.2.10 Shadow Variable.- 1.3 Types of Microdata.- 1.3.1 Simple Microdata.- 1.3.2 Complex Microdata.- 1.4 Types of Tabular Data.- 1.4.1 Single Tables.- 1.4.2 Marginal Tables.- 1.4.3 Hierarchical Tables.- 1.4.4 Linked Tables.- 1.4.5 Semi-linked Tables.- 1.4.6 Complex Tables.- 1.4.7 Tables from Hierarchical Microdata.- 1.5 Introduction to SDC for Microdata and Tables.- 1.6 Intruders and Disclosure Scenarios.- 1.7 Information Loss.- 1.7.1 Information Loss for Microdata.- 1.7.2 Information Loss for Tables.- 1.8 Disclosure Protection Techniques for Microdata.- 1.8.1 Local Recoding.- 1.8.2 Global Recoding.- 1.8.3 Local Suppression.- 1.8.4 Local Suppression with Imputation.- 1.8.5 Synthetic Microdata and Multiple Imputation.- 1.8.6 Subsampling.- 1.8.7 Adding Noise.- 1.8.8 Rounding.- 1.8.9 Microaggregation.- 1.8.10 PRAM.- 1.8.11 Data Swapping.- 1.9 Disclosure Protection Techniques for Tables.- 1.9.1 Table Redesign.- 1.9.2 Cell Suppression.- 1.9.3 Adding Noise.- 1.9.4 Rounding.- 1.9.5 Source Data Perturbation.- 2 Disclosure Risks for Microdata.- 2.1 Introduction.- 2.2 Microdata.- 2.3 Disclosure Scenario.- 2.4 Predictive Disclosure.- 2.5 Re-identification Risk.- 2.6 Risk Per Record and Overall Risk.- 2.7 Population Uniqueness and Unsafe Combinations.- 2.8 Modeling Risks with Discrete Key Variables.- 2.8.1 Direct Approach.- 2.8.2 Model Based Approach.- 2.9 Disclosure Scenarios in Practice.- 2.9.1 Researcher Scenario.- 2.9.2 Hacker Scenario.- 2.10 Combinations to Check.- 2.10.1 A Priori Specified Combinations.- 2.10.2 Data Driven Combinations: Fingerprinting.- 2.11 Practical Safety Criteria for Perturbative Techniques.- 3 Data Analytic Impact of SDC Techniques on Microdata.- 3.1 Introduction.- 3.2 The Variance Impact of SDC Procedures.- 3.3 The Bias Impact of SDC Procedures.- 3.4 Impact of SDC Procedures on Methods of Estimation.- 3.5 Information Loss Measures Based on Entropy.- 3.5.1 Local Recoding.- 3.5.2 Local Suppression.- 3.5.3 Global Recoding.- 3.5.4 PRAM.- 3.5.5 Data Swapping.- 3.5.6 Adding Noise.- 3.5.7 Rounding.- 3.5.8 Microaggregation.- 3.6 Alternative Information Loss Measures.- 3.6.1 Subjective Measures for Non-perturbative SDC Techniques.- 3.6.2 Subjective Measures for Perturbative SDC Techniques.- 3.6.3 Flow Measure for PRAM.- 3.7 MSP for Microdata.- 4 Application of Non-Perturbative SDC Techniques for Microdata.- 4.1 Introduction.- 4.2 Local Suppression.- 4.2.1 MINUCs Introduced.- 4.2.2 Minimizing the Number of Local Suppressions.- 4.2.3 Minimizing the Number of Different Suppressed Categories.- 4.2.4 Extended Local Suppression Models.- 4.2.5 MINUCs and µ-ARGUS.- 4.3 Global Recoding.- 4.3.1 Free Global Recoding.- 4.3.2 Precoded Global Recoding.- 4.4 Global Recoding and Local Suppression Combined.- 5 Application of Perturbative SDC Techniques for Microdata.- 5.1 Introduction.- 5.2 Overview.- 5.3 Adding Noise.- 5.4 Rounding.- 5.4.1 Univariate Deterministic Rounding.- 5.4.2 Univariate Stochastic Rounding.- 5.4.3 Multivariate Rounding.- 5.5 Derivation of PRAM Matrices.- 5.5.1 Preparations.- 5.5.2 Model I: A Two-step Model.- 5.5.3 Model II: A One-step Model.- 5.5.4 Two-stage PRAM.- 5.5.5 Construction of PRAM Matrices.- 5.5.6 Some Comments on PRAM.- 5.6 Data Swapping.- 5.7 Adjustment Weights.- 5.7.1 Disclosing Poststrata.- 5.7.2 Disclosure for Multiplicative Weighting.- 5.7.3 Disclosure Control for Poststrata.- 6 Disclosure Risk for Tabular Data.- 6.1 Introduction.- 6.2 Disclosur e Risk for Tables of Magnitude Tables.- 6.2.1 Linear Sensitivity Measures.- 6.2.2 Dominance Rule.- 6.2.3 Prior-p ost erior Rule.- 6.2.4 Intruder's Knowledge of the Sensitivi ty Crit erion Used.- 6.2.5

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 and tabular data. It discusses what safe data are, and how information loss can be measured.