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Table of Contents
Preface; Overview of the Book; Acknowledgements; Contents; Acronyms; 1 Software; 1.1 Prerequisites; 1.1.1 Installation and Updates; 1.1.2 Install sdcMicro and Its Browser-Based Point-and-Click App; 1.1.3 Updating the SDC Tools; 1.1.4 Help; 1.1.5 The R Workspace and the Working Directory; 1.1.6 Data Types; 1.1.7 Generic Functions, Methods and Classes; 1.2 Brief Overview on SDC Software Tools; 1.3 Differences Between SDC Tools; 1.4 Working with sdcMicro; 1.4.1 General Information About sdcMicro; 1.4.2 S4 Class Structure of the sdcMicro Package; 1.4.3 Utility Functions
1.4.4 Reporting Facilities1.5 The Point-and-Click App sdcApp; 1.6 The simPop package; References; 2 Basic Concepts; 2.1 Types of Variables; 2.1.1 Non-confidential Variables; 2.1.2 Identifying Variables; 2.1.3 Sensitive Variables; 2.1.4 Linked Variables; 2.1.5 Sampling Weights; 2.1.6 Hierarchies, Clusters and Strata; 2.1.7 Categorical Versus Continuous Variables; 2.2 Types of Disclosure; 2.2.1 Identity Disclosure; 2.2.2 Attribute Disclosure; 2.2.3 Inferential Disclosure; 2.3 Disclosure Risk Versus Information Loss and Data Utility; 2.4 Release Types; 2.4.1 Public Use Files (PUF)
2.4.2 Scientific Use Files (SUF)2.4.3 Controlled Research Data Center; 2.4.4 Remote Execution; 2.4.5 Remote Access; References; 3 Disclosure Risk; 3.1 Introduction; 3.2 Frequency Counts; 3.2.1 The Number of Cells of Equal Size; 3.2.2 Frequency Counts with Missing Values; 3.2.3 Sample Frequencies in sdcMicro; 3.3 Principles of k-anonymity and l-diversity; 3.3.1 Simplified Estimation of Population Frequency Counts; 3.4 Special Uniques Detection Algorithm (SUDA); 3.4.1 Minimal Sample Uniqueness; 3.4.2 SUDA Scores; 3.4.3 SUDA DIS Scores; 3.4.4 SUDA in sdcMicro; 3.5 The Individual Risk Approach
3.5.1 The Benedetti-Franconi Model for Risk Estimation3.6 Disclosure Risks for Hierarchical Data; 3.7 Measuring Global Risks; 3.7.1 Measuring the Global Risk Using Log-Linear Models:; 3.7.2 Standard Log-Linear Model; 3.7.3 Clogg and Eliason Method; 3.7.4 Pseudo Maximum Likelihood Method; 3.7.5 Weighted Log-Linear Model; 3.8 Application of the Log-Linear Models; 3.9 Global Risk Measures; 3.10 Quality of the Risk Measures Under Different Sampling Designs; 3.11 Disclosure Risk for Continuous Variables; 3.12 Special Treatment of Outliers When Calculating Disclosure Risks; References
4 Methods for Data Perturbation4.1 Kind of Methods; 4.2 Methods for Categorical Key Variables; 4.2.1 Recoding; 4.2.2 Local Suppression; 4.2.3 Post-randomization Method (PRAM); 4.3 Methods for Continuous Key Variables; 4.3.1 Microaggregation; 4.3.2 Noise Addition; 4.3.3 Shuffling; References; 5 Data Utility and Information Loss; 5.1 Element-Wise Comparisons; 5.1.1 Comparing Missing Values; 5.1.2 Comparing Aggregated Information; 5.2 Element-Wise Measures for Continuous Variables; 5.2.1 Element-Wise Comparisons of Mixed Scaled Variables; 5.3 Entropy; 5.4 Propensity Score Methods
1.4.4 Reporting Facilities1.5 The Point-and-Click App sdcApp; 1.6 The simPop package; References; 2 Basic Concepts; 2.1 Types of Variables; 2.1.1 Non-confidential Variables; 2.1.2 Identifying Variables; 2.1.3 Sensitive Variables; 2.1.4 Linked Variables; 2.1.5 Sampling Weights; 2.1.6 Hierarchies, Clusters and Strata; 2.1.7 Categorical Versus Continuous Variables; 2.2 Types of Disclosure; 2.2.1 Identity Disclosure; 2.2.2 Attribute Disclosure; 2.2.3 Inferential Disclosure; 2.3 Disclosure Risk Versus Information Loss and Data Utility; 2.4 Release Types; 2.4.1 Public Use Files (PUF)
2.4.2 Scientific Use Files (SUF)2.4.3 Controlled Research Data Center; 2.4.4 Remote Execution; 2.4.5 Remote Access; References; 3 Disclosure Risk; 3.1 Introduction; 3.2 Frequency Counts; 3.2.1 The Number of Cells of Equal Size; 3.2.2 Frequency Counts with Missing Values; 3.2.3 Sample Frequencies in sdcMicro; 3.3 Principles of k-anonymity and l-diversity; 3.3.1 Simplified Estimation of Population Frequency Counts; 3.4 Special Uniques Detection Algorithm (SUDA); 3.4.1 Minimal Sample Uniqueness; 3.4.2 SUDA Scores; 3.4.3 SUDA DIS Scores; 3.4.4 SUDA in sdcMicro; 3.5 The Individual Risk Approach
3.5.1 The Benedetti-Franconi Model for Risk Estimation3.6 Disclosure Risks for Hierarchical Data; 3.7 Measuring Global Risks; 3.7.1 Measuring the Global Risk Using Log-Linear Models:; 3.7.2 Standard Log-Linear Model; 3.7.3 Clogg and Eliason Method; 3.7.4 Pseudo Maximum Likelihood Method; 3.7.5 Weighted Log-Linear Model; 3.8 Application of the Log-Linear Models; 3.9 Global Risk Measures; 3.10 Quality of the Risk Measures Under Different Sampling Designs; 3.11 Disclosure Risk for Continuous Variables; 3.12 Special Treatment of Outliers When Calculating Disclosure Risks; References
4 Methods for Data Perturbation4.1 Kind of Methods; 4.2 Methods for Categorical Key Variables; 4.2.1 Recoding; 4.2.2 Local Suppression; 4.2.3 Post-randomization Method (PRAM); 4.3 Methods for Continuous Key Variables; 4.3.1 Microaggregation; 4.3.2 Noise Addition; 4.3.3 Shuffling; References; 5 Data Utility and Information Loss; 5.1 Element-Wise Comparisons; 5.1.1 Comparing Missing Values; 5.1.2 Comparing Aggregated Information; 5.2 Element-Wise Measures for Continuous Variables; 5.2.1 Element-Wise Comparisons of Mixed Scaled Variables; 5.3 Entropy; 5.4 Propensity Score Methods