A Lightweight Privacy-Preserving Participant Selection Scheme for Mobile Crowdsensing

As a new sensing paradigm, mobile crowdsensing (MCS) needs to select task participants that satisfy the sensing requirements to accomplish sensing tasks. How to choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose a lightweight privacy-preserving participant selection (LPPS) scheme for MCS. Specifically, data requesters and task participants utilize the k-anonymity technique to generate anonymous location matrices, then the cloud server computes the Hadamard product for location matrices to judge whether task participants locate in the corresponding sensing areas. Meanwhile, the Paillier algorithm is adopted to ensure sensing data privacy. Finally, both theoretical analysis and performance evaluation demonstrate that the proposed scheme outperforms the existing schemes in terms of security and efficiency.