Spatio-Temporal Similarity based Privacy-Preserving Worker Selection in Mobile Crowdsensing

As one of the most fundamental problems in mobile crowdsensing (MCS), worker selection has drawn significant attention in recent years. However, very few studies consider the workers' spatio- and temporal-coverage for the sensing task. In this paper, we propose a novel top-k worker selection scheme such that the MCS platform can select qualified workers in terms of spatio-temporal similarity. Besides, we design a novel privacy-preserving approach for protecting participants' spatio-temporal information based on the modified Paillier encryption technique. Detailed security analysis showed that the task re-questor's temporal information and the workers' spatio-temporal information are preserved and will not be revealed to any other parties. Extensive experiments are conducted, and the results demonstrate that our scheme outperforms the baseline methods regarding the selection of reliable workers.