Participant Recruitment for Coverage-Aware Mobile Crowdsensing with Location Differential Privacy

Abstract

Mobile crowdsensing is recognized to be a promising paradigm wherein location-based sensing tasks are outsourced to participants carrying mobile devices. A prominent issue of crowdsensing is to guarantee the sensing coverage by appropriately recruiting participating devices, which requires the disclosure of participants' locations and leads to potential location privacy threats. In this paper, we aim to develop a privacy-preserving participant recruiting scheme for mobile crowdsensing, which guarantees the crowdsensing coverage while preserving participants' location differential privacy against a semi-honest crowdsensing aggregator. Briefly, based on the differential private geo-indistinguishability method, we enable candidate participants to locally perturb their location data. With the obfuscated location information, we formulate the crowdsensing coverage optimization as an Integer Program (IP), and develop a $1-(1-1/f)^f$-approximation algorithm, which yields a near-optimal participant recruiting solution. Through extensive simulations, we demonstrate the tradeoff between privacy preservation and crowdsensing utility, and show that satisfactory crowdsensing coverage can be achieved while preserving the participants' differential location privacy.