Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques, 1st Edition (Hardback) book cover

Introduction to Privacy-Preserving Data Publishing

Concepts and Techniques, 1st Edition

By Benjamin C.M. Fung, Ke Wang, Ada Wai-Chee Fu, Philip S. Yu

Chapman and Hall/CRC

376 pages | 55 B/W Illus.

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pub: 2010-08-02
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Description

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

About the Authors

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.

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COM021000
COMPUTERS / Database Management / General
COM021030
COMPUTERS / Database Management / Data Mining
COM053000
COMPUTERS / Security / General