Data privacy: principles and practice

Cover -- Half title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- Authors -- List of Abbreviations -- 1. Introduction to Data Privacy -- 1.1 Introduction -- 1.2 What Is Data Privacy and Why Is It Important? -- 1.2.1 Protecting Sensitive Data -- 1.2.2 Privacy and Anonymity: Two Sides of the Same Coin -- 1.3 Use Cases: Need for Sharing Data -- 1.3.1 Data Mining and Analysis -- 1.3.2 Software Application Testing -- 1.3.3 Business Operations -- 1.4 Methods of Protecting Data -- 1.5 Importance of Balancing Data Privacy and Utility

1.5.1 Measuring Privacy of Anonymized Data -- 1.5.2 Measuring Utility of Anonymized Data -- 1.6 Introduction to Anonymization Design Principles -- 1.7 Nature of Data in the Enterprise -- 1.7.1 Multidimensional Data -- 1.7.1.1 Challenges in Privacy Preservation of Multidimensional Data -- 1.7.2 Transaction Data -- 1.7.2.1 Challenges in Privacy Preservation of Transaction Data -- 1.7.3 Longitudinal Data -- 1.7.3.1 Challenges in Anonymizing Longitudinal Data -- 1.7.4 Graph Data -- 1.7.4.1 Challenges in Anonymizing Graph Data -- 1.7.5 Time Series Data

1.7.5.1 Challenges in Privacy Preservation of Time Series Data -- References -- 2. Static Data Anonymization Part I: Multidimensional Data -- 2.1 Introduction -- 2.2 Classification of Privacy Preserving Methods -- 2.3 Classification of Data in a Multidimensional Data Set -- 2.3.1 Protecting Explicit Identifiers -- 2.3.2 Protecting Quasi-Identifiers -- 2.3.2.1 Challenges in Protecting QI -- 2.3.3 Protecting Sensitive Data (SD) -- 2.4 Group-Based Anonymization -- 2.4.1 k-Anonymity -- 2.4.1.1 Why k-Anonymization? -- 2.4.1.2 How to Generalize Data? -- 2.4.1.3 Implementing k-Anonymization

2.4.1.4 How Do You Select the Value of k? -- 2.4.1.5 Challenges in Implementing k-Anonymization -- 2.4.1.6 What Are the Drawbacks of k-Anonymization? -- 2.4.2 l-Diversity -- 2.4.2.1 Drawbacks of l-Diversity -- 2.4.3 t-Closeness -- 2.4.3.1 What Is t-Closeness? -- 2.4.4 Algorithm Comparison -- 2.5 Summary -- References -- 3. Static Data Anonymization Part II: Complex Data Structures -- 3.1 Introduction -- 3.2 Privacy Preserving Graph Data -- 3.2.1 Structure of Graph Data -- 3.2.2 Privacy Model for Graph Data -- 3.2.2.1 Identity Protection -- 3.2.2.2 Content Protection -- 3.2.2.3 Link Protection

3.2.2.4 Graph Metrics -- 3.3 Privacy Preserving Time Series Data -- 3.3.1 Challenges in Privacy Preservation of Time Series Data -- 3.3.1.1 High Dimensionality -- 3.3.1.2 Background Knowledge of the Adversary -- 3.3.1.3 Pattern Preservation -- 3.3.1.4 Preservation of Statistical Properties -- 3.3.1.5 Preservation of Frequency-Domain Properties -- 3.3.2 Time Series Data Protection Methods -- 3.3.2.1 Additive Random Noise -- 3.3.2.2 Perturbation of Time Series Data Using Generalization: k-Anonymization -- 3.4 Privacy Preservation of Longitudinal Data -- 3.4.1 Characteristics of Longitudinal Data

3.4.1.1 Challenges in Anonymizing Longitudinal Data

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