Privacy-Preserving Auction-based Incentive Mechanism for Mobile Crowdsensing Systems

Abstract

Many people use mobile devices as the basic sensing units since they are cheap and convenient. Several incentive mechanisms have been proposed in past literature to incentivize worker participation in such sensing units. However, existing auction-based privacy-preserving incentive mechanisms only consider asymptotically truthful property apart from exactly dominant strategy truthful property while maximizing crowdsourcers' revenue. Past research has shown that a single mechanism model is difficult to solve this problem, so we designed a novel hybrid mechanism with differential privacy to strike the balance between utility property and truthful bidding property. The hybrid mechanism, denoted as PQHM, consists of price-privacy auction-based mechanism (denoted as PPAM) and quality-privacy auction-based mechanism (denoted as QPAM). PPAM can get great social utility and QPAM can get truthful bidding property. We could randomize between running the two mechanisms according to a probability distribution (defined as $\lambda$ ) to guarantee the reasonable utility for the crowdsourcers, and give each bidder a strict positive incentive to report truthfully. This paper found that the hybrid incentive mechanism has the characteristics of differential private, exactly truthful, individual rationality, reasonable platform profitability, and calculation efficiency. Furthermore, the selection probability $\lambda$ of PQHM is linearly controllable.