Dynamic Differential Privacy for Location based Applications

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Provided by: Cornell University
Topic: Mobility
Format: PDF
Concerns on location privacy frequently arise with the rapid development of GPS enabled devices and location-based applications. While spatial transformation techniques such as location perturbation or generalization have been studied extensively, most techniques rely on syntactic privacy models without rigorous privacy guarantee. Many of them only consider static scenarios or perturb the location at single timestamps without considering temporal correlations of a moving user's locations, and hence are vulnerable to various inference attacks. In this paper, the authors propose a systematic solution for continual location sharing with rigorous differential privacy guarantee.
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