P²Ride: Practical and Privacy-Preserving Ride-Matching Scheme for Ridesharing

As a popular instance of sharing economy, ridesharing has been widely adopted in recent years. To use the convenient ridesharing service, riders and drivers have to share with the service provider their private trip information, which impedes users from freely enjoying the benefits of ridesharing. However, existing studies in ridesharing mainly focus on the optimization of rider-driver matching but ignore the protection of privacy of users. In this paper, we propose P2Ride, a Practical and Privacy-preserving Ride-matching scheme for ridesharing, which enables the service provider to efficiently match drivers with appropriate riders without learning the privacy of both drivers and riders. In P2Ride, we first convert the complex ride-matching computation into equality testing by leveraging overlapping partition systems, and then achieve the privacy-preserving ride-matching by designing a novel non-interactive private equality testing protocol. We prove the security of the proposed P2Ride theoretically. Moreover, a prototype of the P2Ride is implemented, and the experiment results over a real-world dataset demonstrate that the proposed P2Ride can achieve both high ride-matching accuracy and practical efficiency.