pRide: Privacy-Preserving Ride Matching Over Road Networks for Online Ride-Hailing Service

An online ride-hailing (ORH) service, such as Uber and Didi Chuxing, can provide on-demand transportation service to users via mobile phones, which brings great convenience to people's daily lives. Along with the convenience, high privacy concerns are also raised when using an ORH service since users and drivers must share their real-time locations with the ORH server, which results in the leakage of the mobility patterns and additional privacy of users and drivers. In this paper, we propose a privacy-preserving ride-matching scheme, called pRide, for ORH service. pRide allows an ORH server to efficiently match rider and drivers based on their distances in the road network without revealing the location privacy of riders and drivers. Specifically, we make use of the road network embedding technique together with cryptographic primitives and design a scheme to securely and efficiently estimate the shortest distances between riders and drivers in road networks approximately. Moreover, by incorporating garbled circuits, the proposed scheme is able to output the nearest driver around a rider. We implement the scheme and evaluate it on the representative real-world datasets. The theoretical analysis and experimental results demonstrate that pRide achieves an efficient, secure, and yet accurate ride matching for ORH service.