Bilateral Privacy-Preserving Truthful Incentive for Mobile Crowdsensing
Ying Zhong, Xinglin Zhang
Abstract
Reverse auction-based incentive mechanisms have been widely adopted to stimulate mobile workers to participate in mobile crowdsensing (MCS), where workers need to provide location information for winner selection. However, most existing mechanisms rely on trusted platforms. The worker’s location data uploaded to platforms thus can be easily exposed. Recent works start to incorporate location privacy in designing incentive mechanisms, but they have not considered the task’s location privacy and the worker’s location privacy simultaneously. Therefore, we propose a bilateral location privacy-preserving mechanism for MCS on untrusted platforms. In our model, each worker adopts differential privacy to obfuscate his location locally and then submits the obfuscated location together with the bid information. Besides, instead of the exact location, the task requester is only required to upload the task profile and the task’s obfuscated location. Then, we propose the lowest-cost winner selection mechanism which aims to minimize the social cost of winner selection under the location constraint while ensuring task quality requirements, and adopt the critical payment determination mechanism to determine the payments for the winners, which satisfies truthfulness, individual rationality, and computational efficiency. Theoretical analysis and extensive experiments on real-world datasets show the effectiveness of the proposed mechanisms.