Location Trace Privacy Under Conditional Priors

TL;DR

This paper introduces a Rénnyi differential privacy framework tailored for location data with dependent points, providing a method to bound privacy loss and preserve user privacy within a fixed radius.

Contribution

It develops a novel privacy framework for conditionally dependent location data and presents an algorithm for achieving this privacy under Gaussian process priors.

Findings

Framework effectively bounds privacy loss in dependent data scenarios

Algorithm achieves privacy preservation within a fixed radius

Highlights challenges of protecting conditionally dependent location data

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.