Bayesian Analysis of Privacy Attacks on GPS Trajectories

TL;DR

This paper applies Bayesian methods to analyze privacy risks in GPS trajectory sharing, focusing on home-identification attacks and evaluating privacy-region strategies through simulations.

Contribution

It introduces a Bayesian framework for analyzing privacy attacks on GPS data and assesses the effectiveness of privacy-region strategies.

Findings

Bayesian approach effectively models privacy risks.

Privacy-region strategies can mitigate home-identification attacks.

Simulation results demonstrate strategy performance.

Abstract

The success of applications for sharing GPS trajectories raises serious privacy concerns, in particular about users' home addresses. In this paper we show that a Bayesian approach is natural and effective for a rigorous analysis of home-identification attacks and their countermeasures, in terms of privacy. We focus on a family of countermeasures named "privacy-region strategies", consisting in publishing each trajectory from the first exit to the last entrance from/into a privacy region. Their performance is studied through simulations on Brownian motions.