Introduction to Privacy-Preserving Data Publishing:Concepts and Techniques

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.

Benjamin C. M. Fung is an assistant professor in the Concordia Institute for Information Systems Engineering at Concordia University in Montreal, Quebec. Dr. Fung is also a research scientist and the treasurer of the National Cyber-Forensics and Training Alliance Canada (NCFTA Canada).
Ke Wang is a professor in the School of Computing Science at Simon Fraser University in Burnaby, British Columbia.
Ada Wai-Chee Fu is an associate professor in the Department of Computer Science and Engineering at the Chinese University of Hong Kong.
Philip S. Yu is a professor in the Department of Computer Science and the Wexler Chair in Information and Technology at the University of Illinois at Chicago.

THE FUNDAMENTALS IntroductionData Collection and Data Publishing What Is Privacy-Preserving Data Publishing? Related Research Areas

Attack Models and Privacy Models Record Linkage ModelAttribute Linkage ModelTable Linkage Model Probabilistic ModelModeling Adversary’s Background Knowledge

Anonymization Operations Generalization and Suppression Anatomization and Permutation Random Perturbation

Information Metrics General Purpose MetricsSpecial Purpose Metrics Trade-Off Metrics

Anonymization Algorithms Algorithms for the Record Linkage Model Algorithms for the Attribute Linkage ModelAlgorithms for the Table Linkage Model Algorithms for the Probabilistic Attack Attacks on Anonymous Data

ANONYMIZATION FOR DATA MININGAnonymization for Classification Analysis Introduction Anonymization Problems for Red Cross BTSHigh-Dimensional Top-Down Specialization (HDTDS) Workload-Aware Mondrian Bottom-Up Generalization Genetic Algorithm Evaluation Methodology Summary and Lesson Learned

Anonymization for Cluster Analysis Introduction Anonymization Framework for Cluster AnalysisDimensionality Reduction-Based Transformation Related Topics Summary

EXTENDED DATA PUBLISHING SCENARIOS Multiple Views Publishing Introduction Checking Violations of k-Anonymity on Multiple ViewsChecking Violations with Marginals Multi-Relational k-Anonymity Multi-Level Perturbation Summary

Anonymizing Sequential Releases with New Attributes Introduction Monotonicity of Privacy Anonymization Algorithm for Sequential Releases Extensions Summary

Anonymizing Incrementally Updated Data RecordsIntroduction Continuous Data PublishingDynamic Data RepublishingHD-Composition Summary

Collaborative Anonymization for Vertically Partitioned Data Introduction Privacy-Preserving Data MashupCryptographic Approach Summary and Lesson Learned

Collaborative Anonymization for Horizontally Partitioned Data Introduction Privacy Model Overview of the Solution Discussion

ANONYMIZING COMPLEX DATA Anonymizing Transaction Data IntroductionCohesion ApproachBand Matrix Methodkm-AnonymizationTransactional k-AnonymityAnonymizing Query LogsSummary

Anonymizing Trajectory Data Introduction LKC-Privacy (k, δ)-Anonymity MOB k-Anonymity Other Spatio-Temporal Anonymization Methods Summary

Anonymizing Social Networks Introduction General Privacy-Preserving Strategies Anonymization Methods for Social NetworksData SetsSummary

Sanitizing Textual Data Introduction ERASE Health Information DE-identification (HIDE) Summary

Other Privacy-Preserving Techniques and Future TrendsInteractive Query Model Privacy Threats Caused by Data Mining Results Privacy-Preserving Distributed Data Mining Future Directions

References