OP4: An OPPortunistic Privacy-Preserving Scheme for Crowdsensing Applications

Abstract

Crowdsensing applications rely on volunteers to collect sensor readings using their mobile devices. Since the collected sensor readings are annotated with spatiotemporal information, the volunteers' privacy may be endangered. Existing privacy-preserving solutions often disclose the volunteers' location information to either a central third party or their peers. As a result, the volunteers need to trust these parties to respect their privacy. In this paper, we present a distributed approach based on the concept of multi-party computation, which does not require a trusted party and protects the location information against curious users. We evaluate the performance of our approach and show its feasibility by means of extensive simulations based on a real-world dataset. We further implement a proof-of-concept to test its performance under realistic conditions.