BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms
Jian Lin, Dejun Yang, Ming Li, Jia Hao Xu, Guoliang Xue · 2016
With the rapid growth of smartphones, crowdsensing emerges as a new paradigm which takes advantage of the pervasive sensor-embedded smartphones to collect data efficiently. Auction has been widely used to design mechanisms to stimulate smartphone users to participate in the crowdsensing applications and systems. Many auction-based incentive mechanisms have been proposed for crowdsensing. However, none of them has taken into consideration both the bid privacy of smartphone users and the social cost. To the best of our knowledge, we are the first to study the design of privacy-preserving incentive mechanisms that also achieve approximate social cost minimization. In this paper, we design BidGuard, a general privacy-preserving framework for incentivizing crowdsensing. This framework works with different score functions for selecting users. In particular, we propose two score functions, linear and log functions, to realize the framework. We rigorously prove that BidGuard achieves computational efficiency, individual rationality, truthfulness, differential privacy and approximate social cost minimization. In addition, the BidGuard with log score function is asymptotically optimal in terms of the social cost. Extensive simulations evaluate the performance and validate the desired properties of BidGuard.