Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing
This addresses privacy leakage issues for mobile users in crowdsensing applications, though it appears incremental as it builds on existing cryptographic techniques.
The paper tackles the conflict between user privacy and effective task allocation in mobile crowdsensing by proposing SPOON, a scheme that uses proxy re-encryption and BBS+ signatures to protect privacy while enabling accurate recruitment based on location and trust levels, demonstrating efficiency in computation and communication.
Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication.