Chapman & Hall/CRC Press, 2011. — 356 p.The book has four parts. Part I discusses the fundamentals of privacypreserving data publishing. Part II presents anonymization methods for preserving information utility for some specific data mining tasks. The data publishing scenarios discussed in Part I and Part II assume publishing a single data release from one data holder. In real-life data publishing, the scenario is more complicated. For example, the same data may be published several times. Each time, the data is anonymized differently for different purposes, or the data is published incrementally as new data are collected. Part III discusses the privacy issues, privacy models, and anonymization methods for these more realistic, yet more challenging, data publishing scenarios. All works discussed in the first three parts focus on anonymizing relational data. What about other types of data? Recent studies have shown that publishing transaction data, trajectory data, social networks data, and textual data may also result in privacy threats and sensitive information leakages. Part IV studies the privacy threats, privacy models, and anonymization methods for these complex data. This book is designed to provide a detailed overview of the field of privacypreserving data publishing. The materials presented are suitable for an advanced undergraduate course or a graduate course. Alternatively, privacypreserving data publishing can be one of the topics in a database security or a data mining course. This book can serve as a textbook or a supplementary reference for these types of courses.Part I The Fundamentals Introduction Attack Models and Privacy Models Anonymization Operations Information Metrics Anonymization Algorithms Part II Anonymization for Data Mining Anonymization for Classification Analysis Anonymization for Cluster Analysis Part III Extended Data Publishing Scenarios Multiple Views Publishing Anonymizing Sequential Releases with New Attributes Anonymizing Incrementally Updated Data Records Collaborative Anonymization for Vertically Partitioned Data Collaborative Anonymization for Horizontally Partitioned Data Part IV Anonymizing Complex Data Anonymizing Transaction Data Anonymizing Trajectory Data Anonymizing Social Networks Sanitizing Textual Data Other Privacy-Preserving Techniques and Future Trends
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