Location Trace Privacy Under Conditional Priors

Casey Meehan, Kamalika Chaudhuri

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
Rényi 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.