Introduction to Privacy-Preserving Data Publishing : Concepts and Techniques

A gentle introduction for those new to the area, this book presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements. Real-life case studies illustrate the practical challenges of information sharing. The book addresses the privacy issues of relational, transaction, trajectory, social network, and textual data. The authors discuss the assumptions and desirable properties of privacy-preserving data publishing, evaluate various approaches to privacy-preserving data publishing, and explore the differences in privacy-preserving data publishing from related research areas. They also cover applications and future trends.

Gaining access to high-quality data is a vital necessity in knowledge-based decision making. But data in its raw form often contains sensitive information about individuals. Providing solutions to this problem, the methods and tools of privacy-preserving data publishing enable the publication of useful information while protecting data privacy. Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques presents state-of-the-art information sharing and data integration methods that take into account privacy and data mining requirements.

The first part of the book discusses the fundamentals of the field. In the second part, the authors present anonymization methods for preserving information utility for specific data mining tasks. The third part examines the privacy issues, privacy models, and anonymization methods for realistic and challenging data publishing scenarios. While the first three parts focus on anonymizing relational data, the last part studies the privacy threats, privacy models, and anonymization methods for complex data, including transaction, trajectory, social network, and textual data.

This book not only explores privacy and information utility issues but also efficiency and scalability challenges. In many chapters, the authors highlight efficient and scalable methods and provide an analytical discussion to compare the strengths and weaknesses of different solutions.