Verwandte Artikel zu Elements of Statistical Disclosure Control
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Inhaltsangabe
1 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.
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