PGRoute: Practical and Privacy-Preserving Group Ride-Sharing Matching for Online Ride-Hailing Systems

Privacy-preserving online ride-hailing (ORH) services can offer riders and drivers a more enhanced travel experience without disclosing their location privacy. Group ride-sharing is specifically designed for riders undertaking long-distance trips, allowing a group of riders with similar travel plans to share a single taxi. However, the lack of integrated route planning in existing privacy-preserving schemes prevents them from effectively matching riders with the most optimal taxi. In this paper, we propose a privacy-preserving group ride-sharing matching scheme, PGRoute, based on leveled fully homomorphic encryption (LFHE). In PGRoute, we propose a privacy-preserving path planning method and design a fast ciphertext-based distance matrix computation protocol for the key time-consuming modules in it to effectively improve the efficiency. PGRoute can plan routes for groups of riders, determine the optimal boarding order, and match the most suitable taxi while protecting the location privacy of both riders and taxi drivers. Theoretical analysis and experimental results demonstrate that PGRoute is secure and efficient within ORH systems. Compared to previous works, PGRoute reduces the pickup time for a group of riders by a factor of 2.9-7.5× , and achieves 2.7-6.7× higher computational efficiency.