Bayesian Analysis of Privacy Attacks on GPS Trajectories

2018/06/23 by Sirio Legramanti, Legramanti, Sirio
Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #stat.AP

paper · pdf · doi:10.48550/arxiv.1806.08998

6 pages, LaTeX; more concise and partially rewritten with respect to the first version, but same results

arxiv created 2019/05/14 · arxiv updated 2019/05/15

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.

Related

• Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories
• Prolonging the Hide-and-Seek Game: Optimal Trajectory Privacy for Location-Based Services
• Measuring Privacy Loss in Distributed Spatio-Temporal Data
• Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data
• Privacy-Aware Adversarial Network in Human Mobility Prediction