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Introduction to Privacy-Preserving Data Publishing

Concepts and Techniques

Introduction to Privacy-Preserving Data Publishing by Benjamin C.M. Fung
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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

CRC Press; August 2010
374 pages; ISBN 9781420091502
Read online, or download in secure PDF format
Title: Introduction to Privacy-Preserving Data Publishing
Author: Benjamin C.M. Fung; Ke Wang; Ada Wai-Chee Fu; Philip S. Yu
 
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