The Privacy Exposure Problem in Mobile Location-based Services
This addresses privacy risks for mobile users in location-based services, but it is incremental as it builds on existing privacy-preserving methods with a new metric and algorithm.
The paper tackles the problem of privacy preservation in mobile location-based services by proposing a metric called privacy exposure to quantify privacy and an algorithm to minimize it, with simulations and real-world experiments showing that the metric properly quantifies privacy levels and the algorithm effectively cloaks user activity hotspots and transitions.
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.