Privacy-Preserving Cost Minimization in Mobile Crowd Sensing Supported by Edge Computing

To minimize the sensing cost in MCS while preserving the participants' privacy, in this paper we propose a Data Sensing mechanism with User Privacy Preserved (DS-UPP). We introduce edge computing into MCS to support task allocation and user privacy protection. In DS-UPP, based on compressive sensing theory we minimize the amount of data needed to be submitted. We also design an algorithm based on local differential privacy theory. Selected participants only need to submit their real data along with the reconstructed data generated by the algorithm. It is proved that DS-UPP satisfies ε-differential privacy. We give the mathematical lower bound and upper bound of the number of participants needed for task accomplishment with the constraints that privacy budget is ε and recovery error of task data is 0, as well as the average amount of data that should be submitted by a participant. We also evaluate the performance of DS-UPP through simulations. Compared with the existing method PrivKV, DS-UPP can reduce the needed data amount by about 90% on the average while guarantee users' privacy preserved.