Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms

TL;DR

This paper examines the limitations of existing location privacy mechanisms under realistic adversary knowledge and introduces a novel randomized remapping technique to enhance privacy protection.

Contribution

It analyzes the privacy leakage in remapping techniques considering practical adversary assumptions and proposes a new randomized remapping method as an effective countermeasure.

Findings

Remapping leaks privacy about location and statistical models under practical assumptions.

Randomized remapping improves privacy preservation against informed adversaries.

The study highlights the importance of considering adversary knowledge in privacy mechanism design.

Abstract

The prevalence of mobile devices and Location-Based Services (LBS) necessitate the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBS due to the noise they add to users' locations. Here, we consider the remapping technique, which presumes the adversary has a perfect statistical model for the user location. We consider this assumption and show that under practical assumptions on the adversary's knowledge, the remapping technique leaks privacy not only about the true location data, but also about the statistical model. Finally, we introduce a novel solution called "Randomized Remapping" as a countermeasure.