Systematic Evaluation of Geolocation Privacy Mechanisms

TL;DR

This paper systematically evaluates various geolocation privacy mechanisms across different usage scenarios, revealing that their effectiveness varies significantly depending on how users interact with location-based services.

Contribution

It introduces a comprehensive framework for evaluating LPPMs considering multiple scenarios, and compares several mechanisms including a new improved one.

Findings

Privacy of Planar Laplace geo-indistinguishability decreases in continuous scenarios

Scenario-dependent performance of LPPMs highlights the need for tailored privacy solutions

Evaluation framework enables systematic comparison of privacy mechanisms

Abstract

Location data privacy has become a serious concern for users as Location Based Services (LBSs) have become an important part of their life. It is possible for malicious parties having access to geolocation data to learn sensitive information about the user such as religion or political views. Location Privacy Preserving Mechanisms (LPPMs) have been proposed by previous works to ensure the privacy of the shared data while allowing the users to use LBSs. But there is no clear view of which mechanism to use according to the scenario in which the user makes use of a LBS. The scenario is the way the user is using a LBS (frequency of reports, number of reports). In this paper, we study the sensitivity of LPPMs on the scenario on which they are used. We propose a framework to systematically evaluate LPPMs by considering an exhaustive combination of LPPMs, attacks and metrics. Using our framework we compare a selection of LPPMs including an improved mechanism that we introduce. By evaluating over a variety of scenarios, we find that the efficacy (privacy, utility, and robustness) of the studied mechanisms is dependent on the scenario: for example the privacy of Planar Laplace geo-indistinguishability is greatly reduced in a continuous scenario. We show that the scenario is essential to consider when choosing an obfuscation mechanism for a given application.