Locally Differentially Private Participant Recruitment for Mobile Crowdsourcing

Abstract

Location-aware mobile crowdsourcing tasks like urban sensing always require exposing users' location, which lead to serious privacy breaches. In this poster, we propose a locally differentially private participants recruitment system to maximize spatial coverage of the mobile crowdsourcing task while preserving location privacy. Based on the mechanism of randomized response, our system preserves the privacy in a local way, which eliminates the need for a trusted server. With guaranteed location privacy protection, a heuristic algorithm is proposed to solve the maximum spatial coverage problem efficiently given the obfuscated reports. Extensive experiments on real-world user trajectories demonstrate the feasibility of our proposed system, which improves the spatial coverage by more than 10% on average compared with the state-of-the-art solutions.