The Privacy Exposure Problem in Mobile Location-based Services

TL;DR

This paper introduces a new metric called privacy exposure to quantify privacy risks in mobile location-based services and proposes an algorithm to minimize this exposure, validated through simulations and real-world experiments.

Contribution

It presents a novel privacy exposure metric and an algorithm to reduce privacy risks in mobile LBSs, supported by extensive evaluations.

Findings

The metric effectively quantifies privacy exposure levels.

The algorithm successfully cloaks activity hotspots and transitions.

Experimental results confirm the approach's effectiveness across mobility levels.

Abstract

Mobile location-based services (LBSs) empowered by mobile crowdsourcing provide users with context-aware intelligent services based on user locations. As smartphones are capable of collecting and disseminating massive user location-embedded sensing information, privacy preservation for mobile users has become a crucial issue. This paper proposes a metric called privacy exposure to quantify the notion of privacy, which is subjective and qualitative in nature, in order to support mobile LBSs to evaluate the effectiveness of privacy-preserving solutions. This metric incorporates activity coverage and activity uniformity to address two primary privacy threats, namely activity hotspot disclosure and activity transition disclosure. In addition, we propose an algorithm to minimize privacy exposure for mobile LBSs. We evaluate the proposed metric and the privacy-preserving sensing algorithm via extensive simulations. Moreover, we have also implemented the algorithm in an Android-based mobile system and conducted real-world experiments. Both our simulations and experimental results demonstrate that (1) the proposed metric can properly quantify the privacy exposure level of human activities in the spatial domain and (2) the proposed algorithm can effectively cloak users' activity hotspots and transitions at both high and low user-mobility levels.