Location Trace Privacy Under Conditional Priors

Casey Meehan; Kamalika Chaudhuri

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arXiv · 1912.04228

Location Trace Privacy Under Conditional Priors

Casey Meehan·Kamalika Chaudhuri

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Abstract

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a R\'enyi differentially private framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for every user location in a trace.

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Casey Meehan, Kamalika Chaudhuri. 2019-12-09. Location Trace Privacy Under Conditional Priors. https://arxiv.org/abs/1912.04228

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