Читать книгу Database Anonymization - David Sánchez - Страница 8
ОглавлениеContents
2.4 Disclosure Risk in Microdata Sets
2.6 Measuring Information Loss
2.7 Trading Off Information Loss and Disclosure Risk
3 Anonymization Methods for Microdata
3.1 Non-perturbative Masking Methods
3.2 Perturbative Masking Methods
3.3 Synthetic Data Generation
3.4 Summary
4 Quantifying Disclosure Risk: Record Linkage
4.1 Threshold-based Record Linkage
4.2 Rule-based Record Linkage
4.3 Probabilistic Record Linkage
4.4 Summary
5 The k-Anonymity Privacy Model
5.1 Insufficiency of Data De-identification
5.2 The k-Anonymity Model
5.3 Generalization and Suppression Based k-Anonymity
5.4 Microaggregation-based k-Anonymity
5.5 Probabilistic k-Anonymity
5.6 Summary
6 Beyond k-Anonymity: l-Diversity and t-Closeness
6.1 l-Diversity
6.2 t-Closeness
6.3 Summary
7 t-Closeness Through Microaggregation
7.1 Standard Microaggregation and Merging
7.2 t-Closeness Aware Microaggregation: k-anonymity-first
7.3 t-Closeness Aware Microaggregation: t-closeness-first
7.4 Summary
8.1 Definition
8.2 Calibration to the Global Sensitivity
8.3 Calibration to the Smooth Sensitivity
8.4 The Exponential Mechanism
8.5 Relation to k-anonymity-based Models
8.6 Differentially Private Data Publishing
8.7 Summary
9 Differential Privacy by Multivariate Microaggregation
9.1 Reducing Sensitivity Via Prior Multivariate Microaggregation
9.2 Differentially Private Data Sets by Insensitive Microaggregation
9.3 General Insensitive Microaggregation
9.4 Differential Privacy with Categorical Attributes
9.5 A Semantic Distance for Differential Privacy
9.6 Integrating Heterogeneous Attribute Types
9.7 Summary
10 Differential Privacy by Individual Ranking Microaggregation
10.1 Limitations of Multivariate Microaggregation
10.2 Sensitivity Reduction Via Individual Ranking
10.3 Choosing the Microggregation Parameter k
10.4 Summary
11 Conclusions and Research Directions
11.1 Summary and Conclusions
11.2 Research Directions
