Ensuring Privacy in Location-Based Services: A Model-based Approach

TL;DR

This paper introduces a model-based privacy-preserving framework for location-based services, using Markov decision processes to quantify and guarantee user privacy while maintaining service utility, validated with real-world data.

Contribution

It models user mobility and service utility as a Markov decision process and proposes a new privacy metric, ε-privacy, to ensure user privacy over an infinite horizon.

Findings

The proposed LPPM guarantees a user-specified privacy level.

The framework maintains service utility while protecting user location.

Validated with real-world mobility data.

Abstract

In recent years, the widespread of mobile devices equipped with GPS and communication chips has led to the growing use of location-based services (LBS) in which a user receives a service based on his current location. The disclosure of user's location, however, can raise serious concerns about user privacy in general, and location privacy in particular which led to the development of various location privacy-preserving mechanisms aiming to enhance the location privacy while using LBS applications. In this paper, we propose to model the user mobility pattern and utility of the LBS as a Markov decision process (MDP), and inspired by probabilistic current state opacity notation, we introduce a new location privacy metric, namely $\epsilon-$privacy, that quantifies the adversary belief over the user's current location. We exploit this dynamic model to design a LPPM that while it ensures the utility of service is being fully utilized, independent of the adversary prior knowledge about the user, it can guarantee a user-specified privacy level can be achieved for an infinite time horizon. The overall privacy-preserving framework, including the construction of the user mobility model as a MDP, and design of the proposed LPPM, are demonstrated and validated with real-world experimental data.