Secure and Privacy-Preserving Report De-duplication in the Fog-Based Vehicular Crowdsensing System

Nowadays, vehicles are powerful enough to carry communications, computing and storage capabilities. By interacting with each other and with local (i.e., fog) infrastructures like road-side units, a cohort of vehicles and fog devices could collaboratively provide services like crowdsensing in an unprecedentedly secure and efficient way. However, it has been widely recognized as a challenging work in the vehicular system to develop a secure and efficient sensing task allocation and data de-duplication mechanism. In this paper, we attempt to develop a scheme to address this challenge. Specifically, we use the Elliptic Curves Cryptography (ECC) algorithm to realize secure allocation of location-dependent tasks. During the report submission phase, we adopt the improved message-lock encryption to realize privacy-preserving data de-duplication and to resist the duplicate-faking attacks. Besides, we present a novel signature scheme that can efficiently record the contributions of each vehicle. The security analysis and performance evaluation demonstrate that the proposed scheme can achieve secure and privacy-preserving report de-duplication with moderate computation and communication overhead.