Incentive And Privacy-Preserving Mechanism Design For Mobile Crowd Sensing Systems

With the popularization of human-carried devices(such as smart phone,wearable device),it is possible for Mobile Crowd Sensing(MCS)applications to perform large scale sensing with sensors embedded in these devices.Due to the much fewer sens-ing costs and much higher sensing coverage compared to traditional sensing networks,there are many application scenarios in our daily life concerning healthcare,smart trans-portation and so on.Nowadays,there are still some important issues in the MCS that need to be resolved,including ensuring data sources,ensuring data reliability and pro-tecting data privacy.However,many researchers did not consider the limited nature of platform budgets and user-provided data.Therefore,designing an MCS system with comprehensive considerations which can collect as much reliable data as possible has important research significance.In the paper,we design incentive mechanisms based on reverse auction which ensure approximately maximized value of services provided by selected workers and ensure honest bids submitted by workers.Meanwhile,these two mechanisms consider situations where there are constraints on both crowdsourcer’s budget and workers’ ca-pacity.In addition,we also use differential privacy technology to protect users’ data privacy.Not only do we study the scenario where every worker is trusted and will not infer others’ bids,but we also investigate the scenario where there are some honest-but-curious workers.For the former,we design a truthful,individual rational,and computationally efficient incentive mechanism that achieves nearly optimal benefits.For the latter,we design an approximately truthful,individual rational,computationally efficient,and differentially private incentive mechanism that helps to protect workers’privacy from the infringement of curious workers and achieves nearly optimal benefits.Rigorous theoretical analyses and extensive simulations are given to validate the above properties and evaluate the performance of our incentive mechanisms.