The Privacy Exposure Problem in Mobile Location-based Services
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.