Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applications. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant selection problems solved with auction-based approaches usually involve participants' privacy concerns because a participant's bids may contain her private information (such as location visiting patterns), and disclosure of participants' bids may disclose their private information as well. In this paper, we study how to protect such bid privacy in a temporally and spatially dynamic MCS system. We assume that both sensing tasks and mobile participants have dynamic characteristics over spatial and temporal domains. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, where participants are grouped into multiple participant groups and then auctions are organized within groups via secure group bidding. By leveraging Lagrange polynomial interpolation to perturb participants' bids within groups, participants' bid privacy is preserved. In addition, the proposed solution does not affect the operation of current MCS platform since the groups act as regular users to the platform. Both theoretical analysis and real-life tracing data simulations verify the efficiency and security of the proposed solution.