Introduction to Privacy-Preserving Data Publishing

Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques

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Features

  • Presents a gentle introduction to privacy-preserving data publishing for those new to the area
  • Contains real-life case studies that illustrate the practical challenges of information sharing
  • Discusses the assumptions and desirable properties of privacy-preserving data publishing
  • Addresses the privacy issues of relational, transaction, trajectory, social network, and textual data
  • Explores the differences in privacy-preserving data publishing from related research areas
  • Evaluates various approaches to privacy-preserving data publishing
  • Covers applications and future trends of privacy-preserving data publishing

 

Summary

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.

Table of Contents

THE FUNDAMENTALS
Introduction

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

Attack Models and Privacy Models
Record Linkage Model
Attribute Linkage Model
Table Linkage Model
Probabilistic Model
Modeling Adversary’s Background Knowledge

Anonymization Operations
Generalization and Suppression
Anatomization and Permutation
Random Perturbation

Information Metrics
General Purpose Metrics
Special Purpose Metrics
Trade-Off Metrics

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

ANONYMIZATION FOR DATA MINING
Anonymization for Classification Analysis

Introduction
Anonymization Problems for Red Cross BTS
High-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 Analysis
Dimensionality Reduction-Based Transformation
Related Topics
Summary

EXTENDED DATA PUBLISHING SCENARIOS
Multiple Views Publishing
Introduction
Checking Violations of k-Anonymity on Multiple Views
Checking 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 Records
Introduction
Continuous Data Publishing
Dynamic Data Republishing
HD-Composition
Summary

Collaborative Anonymization for Vertically Partitioned Data
Introduction
Privacy-Preserving Data Mashup
Cryptographic 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
Introduction
Cohesion Approach
Band Matrix Method
km-Anonymization
Transactional k-Anonymity
Anonymizing Query Logs
Summary

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 Networks
Data Sets
Summary

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

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

References

Author Bio(s)

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.

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