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 | |
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