Towards Privacy-driven Truthful Incentives for Mobile Crowdsensing Under Untrusted Platform

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing. Recent works pointed out that bid is a private information and proposed bidding-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection when the platform is untrusted. In this paper, we design novel privacy-preserving incentive mechanisms to protect users' true bid information against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated bids to the platform. Two solutions are proposed for the platform to solve the winner selection problem with the obfuscated information. Moreover, we further propose a novel task-bid pair protection truthful incentive mechanism to further prevent privacy leakage from the set of interested tasks, where each user encrypts his interested tasks locally and an encrypted task clustering method is proposed for winner selection with users' encrypted task-bid pairs. Both of theoretical analysis and extensive experiments demonstrate the effectiveness of proposed mechanisms.