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Table of contents
1 Introduction
1.1 They key role of privacy research
1.2 Location Privacy
1.3 State of the Art
1.4 Contributions
1.5 Publications
1.6 Plan of the thesis
2 State of the Art
2.1 k-anonymity
2.1.1 l-diversity and t-closeness
2.1.2 Background knowledge
2.2 Differential Privacy
2.2.1 Compositionality and Budget
2.2.2 Background Knowledge
2.3 Bayesian Approach
2.3.1 min-entropy
2.3.2 g-leakage
2.3.3 Relation with Differential Privacy
2.4 Location Privacy
2.4.1 Location Data
2.4.2 Attacks
2.4.3 k-anonymity
2.4.4 Differential Privacy
2.4.5 Bayesian metrics for Location Privacy
2.4.6 Others metrics
3 Preliminaries
3.1 Location Privacy through reduced Accuracy
3.2 Privacy Definitions
3.2.1 dX -privacy
3.2.2 Geo-indistinguishability
3.2.3 Distinguishability level and
3.2.4 Traces
3.3 Privacy Mechanisms
3.3.1 Laplace Mechanism
3.3.2 Planar Laplace Mechanism
3.3.3 Exponential Mechanism
3.3.4 Independent Mechanism
3.4 Utility
3.4.1 Expected Error
3.4.2 ()-accuracy
4 Repeated Use over Time
4.1 Introduction
4.2 A Predictive dX -private Mechanism
4.2.1 Budget management
4.2.2 The mechanism
4.2.3 Privacy
4.2.4 Utility
4.2.5 Skipping the test
4.3 Predictive mechanism for location privacy
4.3.1 Prediction Function
4.3.2 Budget Managers
4.3.3 Configuration of the mechanism
4.3.4 Evaluation
4.3.5 Future Work
4.4 Incorporating fences in the metric
4.4.1 Fences
4.4.2 Mechanism
4.4.3 Future work
4.5 Conclusion
5 Flexible Use over Space
5.1 Introduction
5.2 An elastic distinguishability metric
5.2.1 Privacy mass
5.2.2 Requirement
5.2.3 Extracting location quality
5.2.4 An efficient algorithm to build elastic metrics
5.3 Evaluation
5.3.1 Metric construction for Paris’ metropolitan area
5.3.2 Evaluation using the Gowalla and Brightkite datasets
5.4 Tiled Mechanism
5.5 Conclusion
6 Location Guard
6.1 A web browser extension
6.2 Desktop and mobile
6.3 Operation
6.4 Adoption
6.5 Future Work
7 Conclusion
