Participant Grouping for Privacy Preservation in Mobile Crowdsensing over Hierarchical Edge Clouds

In mobile crowdsensing (MCS), to select the optimal set of participants for a particular sensing task, the cloud-based MCS platform requires mobile users to submit their bids and their sensing quality data. This can cause privacy breaches. One possible solution is to leverage secure sharing or bidding schemes to protect participants' personal information during selection. However, these schemes suffer from high overheads, poor scalability and more importantly, the group formation has never been studied. To address this issue and to enhance the protection of user privacy, we propose a set of novel privacy-preserving grouping methods, which place participants into small groups over hierarchical edge clouds. By doing this, not only can the participants be hidden in groups, but also the overall privacy-preserving participant selection becomes more scalable. The design goal to minimize the communication cost during secure sharing/bidding within groups, while satisfying each participant's requirement for privacy preservation. For different scenarios and optimization functions, we propose a set of grouping schemes to fulfill this goal. Extensive simulations over both synthetic and real-life datasets illustrate the efficiency of proposed mechanisms.