Achieve Secure and Efficient Skyline Computation for Worker Selection in Mobile Crowdsensing
Xichen Zhang, Rongxing Lu, Jun Shao, Hui Zhu, Ali Akbar Ghorbani · 2019
Worker selection is always one of the fundamental issues in Mobile Crowdsensing (MCS) applications. However, the selection of reliable workers still pose big challenges to the MCS platform, due to either the large number of candidates or the dynamic nature of participating workers. In this paper, aiming at addressing the above challenges, we propose a privacy-preserving worker selection scheme based on (probabilistic) skyline computation technique. Our proposed scheme is characterized by selecting a subset of reliable and suitable workers for a certain task without revealing the workers' relevant personal information. Security analysis shows the proposed scheme can achieve the workers' privacy-preservation. In addition, the performance evaluation also validates its efficiency and effectiveness.