Traceable and Privacy-Preserving Worker Selection Scheme with Arbitrary Spatial Ranges in MCS

Worker selection that is often outsourced to a cloud server is crucial for the success of the Mobile Crowdsensing (MCS) system, in which the spatial constraint plays a fundamental role in selecting workers. To protect the location information involved in the spatial constraint, several privacy-preserving worker selection schemes were proposed. However, they either only support a specific spatial range or cannot trace the workers who leak secret keys. In this paper, we propose a traceable and privacy-preserving worker selection scheme that can support arbitrary spatial ranges when selecting workers and trace the worker when his/her secret key is leaked to the cloud server for inferring location information. Security analysis demonstrates the privacy preservation and traceability of our proposed scheme, and the evaluation results illustrate its efficiency.