Privacy-preserving Worker Selection in Mobile Crowdsensing over Spatial-temporal Constraints

Worker selection is one of the most fundamental problems in Mobile Crowdsensing (MCS) applications. In this paper, we formulate a practical worker selection scenario in MCS services where the selected workers should meet both the spatial and temporal constraints. To protect participants’ (both the task requestor and the workers) personal information from being disclosed, we design a privacy-preserving worker selection scheme based on the Symmetric Homomorphic Encryption (SHE) technique. Besides, we devise a pre-filtering process to further increase the efficiency of the worker assignment process. Security analysis shows that our proposed scheme can achieve the desirable security properties. In addition, extensive experiments are conducted to validate the effectiveness of the proposed scheme.