Bilateral Privacy-Preserving Task Assignment with Personalized Participant Selection for Mobile Crowdsensing.

Mobile crowdsensing (MCS) as an emerging data collection paradigm allows people to collect data for more effective decision-making. Task assignment as an integral part of MCS plays an important role in the working of the system. However, the balance between system efficiency and result accuracy is still a challenge to be solved, while the privacy of requesters and task participants are needed to be considered during assigning tasks. This paper proposes a bilateral privacy-preserving task assignment scheme with personalized participant selection for MCS. With the design of a privacy-preserving top-k selection sub-protocol, the proposed scheme supports the task requester to personalize the selection of participants for task assignment. The balance between efficiency and accuracy is entirely determined by the preference of the task requester. The proposed scheme provides the protections of the privacy of both the task content and personalized parameters of the requester and the status and identity of the participants. Simulation experiments are performed on smart devices with a real-world dataset, and the results demonstrate the effectiveness of the proposed task assignment scheme compared to the previous work.