An Efficient and Privacy-Preserving Participant Selection Scheme based on Location in Mobile Crowdsensing

With the increasing demand for sensing data, the selection efficiency of task participants in mobile crowdsensing (MCS) becomes more and more important. How to efficiently choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose an efficient and privacy-preserving participant selection scheme based on location in MCS, named EPPS. Specifically, the k-anonymity technique is adopted to generate anonymous location matrices to preserve location privacy of sensing tasks and task participants. To improve selection efficiency, the anonymous location matrices of multiple sensing tasks are consolidated into a matrix called merged location matrix. Similarly, the anonymous location matrices of multiple task participants are also merged. Then, the cloud server computes the Hadamard product for merged location matrices to judge whether task participants locate in the corresponding sensing areas. By judging the merged location matrices of sensing tasks and task participants, multiple participants which satisfy the requirements of sensing tasks can be selected at the same time. Finally, the performance evaluations demonstrate that the proposed scheme outperforms the existing schemes in terms of efficiency, to be specific, the computation overhead of EPPS is decreased by 77% - 85%, the communication overhead of EPPS is decreased by 41%, the selection efficiency of the EPPS is increased by 66% when multiple task participants and one sensing tasks, and the selection efficiency of the EPPS is increased by 96% when multiple task participants and multiple sensing tasks.