LFPS: A Blockchain-Based Fair Privacy Preservation Scheme for Mobile Crowdsensing

Mobile Crowd Sensing (MCS) is an emerging research field that leverages mobile devices and vehicles for performing various sensing tasks. MCS enables efficient and cost-effective completion of tasks like road and air quality monitoring. Despite continuous advancements in MCS, several challenges remain to be addressed. In this paper, we present a lightweight, fair, and privacy-protecting scheme, dubbed LFPS, to address two challenges in MCS. Firstly, to resist unfair vehicle selection, LFPS utilizes a blockchain-based fair selection algorithm to generate a public and verifiable vehicle list, where the blockchain system acts as a public verifiable bulletin board and an unpredictable source of randomness. Secondly, to prevent attackers from inferring private information by linking vehicles’ uploaded data across different tasks, LFPS introduces an anonymous token mechanism, which allows vehicles to submit their sensing data anonymously. This mechanism is lightweight, as it does not require expensive cryptographic tools. We give a comprehensive security analysis to demonstrate LFPS’s resistance against various attacks. We also evaluate the performance of LFPS to show its efficiency in terms of computational and communication overheads.