Privacy-preserving reputation management for edge computing enhanced mobile crowdsensing

IEEE Mobile crowdsensing (MCS) has gained popularity for its potential to leverage millions of individual mobile devices to sense, collect, and analyze data instead of deploying thousands of static sensors. As the sensing data become increasingly fine-grained and complicated, there is a tendency to enhance MCS with the edge computing paradigm to reduce time delays and high bandwidth costs. The sensing data may reveal personal information, and thus it is of great significance to preserve the privacy of the participants. However, preserving privacy may hinder the process of handling malicious participants. In this paper, we propose two privacy preserving reputation management schemes for edge computing enhanced MCS to simultaneously preserve privacy and deal with malicious participants. In the basic scheme, a novel reputation value updating method is designed based on the deviations of the encrypted sensing data from the final aggregating result. The basic scheme is efficient at the expense of revealing the deviation value of each participant to the reputation manager. To conquer this drawback, we propose an advanced scheme by updating reputation values utilizing the rank of deviations. Extensive experiments demonstrate that both these two schemes have high cost efficiency and are effective to deal with malicious participants.