Abstract
With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users’ personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users’ location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.
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Acknowledgement
This work is financially supported by NSFC under Grant No. 62102035, and HKRGC under Grant No. CityU 11213920.
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Xie, H., Chen, Z., Guo, Y., Liu, Q., Jia, X. (2022). Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server. In: Yuan, X., Bai, G., Alcaraz, C., Majumdar, S. (eds) Network and System Security. NSS 2022. Lecture Notes in Computer Science, vol 13787. Springer, Cham. https://doi.org/10.1007/978-3-031-23020-2_24
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