Maintained by
Qi Xu, Hui Zhu, Yandong Zheng, Jiaqi Zhao, Rongxing Lu, and Hui Li
Xidian University
266 Xinglong Section of Xifeng Road, Xi'an, Shaanxi 710126, China


With the popularity of intelligent terminals and the advances of mobile Internet, carpooling service, which reduces the travel cost of each user by allowing multiple users to share one car, has received considerable attention and make our life more convenient. However, the vigorous development of carpooling services still faces severe challenges in users' location or route privacy. In this paper, we propose an efficient and privacy-preserving route matching scheme called TAROT for carpooling services. With TAROT, users can enjoy high-quality carpooling services while without revealing sensitive location and route information. Specifically, based on a Goldwasser-Micali based Equality Determination algorithm, we design an Accurate Similarity Computation algorithm, which allows users to get accurate carpooling results over ciphertexts. Meanwhile, the Reverse Minhash method is also designed to construct a Dissimilar Route Filter algorithm, which can filter out dissimilar routes in advance and reduce computational costs and communication overheads. Privacy analysis shows that TAROT can protect users' location privacy. In addition, TAROT is also evaluated with many random maps, and the simulation results demonstrate that TAROT is highly-efficient.

  Conceptual Architecture of Carpooling Service

  System Model Under Consideration

  Parameter Selection

  Computational Costs of TAROT

  Communication Overheads of TAROT

  Dataset Download

Dataset Download:  
Dataset File SHA256: 8B22D3F715C02BEF9702287A956B8847FA815234CDB07A57CB4F38FECCB0CA8E
For Source Code:    zhuhui@xidian.edu.cn