5.8.1 k-Anonymity and Anonymity Sets: k-Confusion.

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Preface; Organization; How to Use This book; Acknowledgements; Contents; 1 Introduction; 1.1 Motivations for Data Privacy; 1.2 Privacy and Society; 1.3 Terminology; 1.3.1 The Framework; 1.3.2 Anonymity and Unlinkability; 1.3.3 Disclosure; 1.3.4 Undetectability and Unobservability; 1.3.5 Pseudonyms and Identity; 1.3.6 Transparency; 1.4 Privacy and Disclosure; 1.5 Privacy by Design; 2 Machine and Statistical Learning; 2.1 Classification of Techniques; 2.2 Supervised Learning; 2.2.1 Classification; 2.2.2 Regression; 2.2.3 Validation of Results: k-Fold Cross-Validation; 2.3 Unsupervised Learning.

2.3.1 Clustering; 2.3.2 Association Rules; 2.3.3 Expectation-Maximization Algorithm ; 3 On the Classification of Protection Procedures; 3.1 Dimensions; 3.1.1 On Whose Privacy Is Being Sought; 3.1.2 On the Computations to be Done; 3.1.3 On the Number of Data Sources; 3.1.4 Knowledge Intensive Data Privacy ; 3.1.5 Other Dimensions and Discussion; 3.1.6 Summary; 3.2 Respondent and Holder Privacy ; 3.3 Data-Driven Methods ; 3.4 Computation-Driven Methods ; 3.4.1 Single Database: Differential Privacy; 3.4.2 Multiple Databases: Cryptographic Approaches; 3.4.3 Discussion.

3.5 Result-Driven Approaches 3.6 Tabular Data; 3.6.1 Cell Suppression; 3.6.2 Controlled Tabular Adjustment; 4 User's Privacy; 4.1 User Privacy in Communications; 4.1.1 Protecting the Identity of the User; 4.1.2 Protecting the Data of the User; 4.2 User Privacy in Information Retrieval; 4.2.1 Protecting the Identity of the User; 4.2.2 Protecting the Query of the User; 4.3 Private Information Retrieval; 4.3.1 Information-Theoretic PIR with k Databases; 4.3.2 Computational PIR; 4.3.3 Other Contexts; 5 Privacy Models and Disclosure Risk Measures; 5.1 Definition and Controversies.

5.1.1 A Boolean or Measurable Condition5.2 Attribute Disclosure; 5.2.1 Attribute Disclosure for a Numerical Variable; 5.2.2 Attribute Disclosure for a Categorical Variable; 5.3 Identity Disclosure; 5.3.1 An Scenario for Identity Disclosure; 5.3.2 Measures for Identity Disclosure; 5.3.3 Uniqueness; 5.3.4 Reidentification; 5.3.5 The Worst-Case Scenario; 5.4 Matching and Integration: A Database Based Approach; 5.4.1 Heterogenous Distributed Databases; 5.4.2 Data Integration; 5.4.3 Schema Matching; 5.4.4 Data Matching; 5.4.5 Preprocessing; 5.4.6 Indexing and Blocking.

5.4.7 Record Pair Comparison: Distances and Similarities; 5.4.8 Classification of Record Pairs; 5.5 Probabilistic Record Linkage; 5.5.1 Alternative Expressions for Decision Rules; 5.5.2 Computation of Rp(a, b); 5.5.3 Estimation of the Probabilities; 5.5.4 Extensions for Computing Probabilities; 5.5.5 Final Notes; 5.6 Distance-Based Record Linkage; 5.6.1 Weighted Distances; 5.6.2 Distance and Normalization; 5.6.3 Parameter Determination for Record Linkage; 5.7 Record Linkage Without Common Variables; 5.8 k-Anonymity and Other Boolean Conditions for Identity Disclosure.

This book offers a broad, cohesive overview of the field of data privacy. It discusses, from a technological perspective, the problems and solutions of the three main communities working on data privacy: statistical disclosure control (those with a statistical background), privacy-preserving data mining (those working with data bases and data mining), and privacy-enhancing technologies (those involved in communications and security) communities. Presenting different approaches, the book describes alternative privacy models and disclosure risk measures as well as data protection procedures for respondent, holder and user privacy. It also discusses specific data privacy problems and solutions for readers who need to deal with big data.