Scalable privacy-preserving participant selection in mobile crowd sensing

Auction based participant selection has been widely used for mobile crowd sensing (MCS) to achieve user incentive and assignment optimization. However, mobile crowd sensing 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 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, which leverages Lagrange polynomial interpolation to perturb participants' bids within groups. The proposed solution does not affect the operation of current MCS platform. Both theoretical analysis and real-life tracing data simulations verify the efficiency and security of the proposed solution.

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