sesa 17(11): e1

Research Article

Re-identification of Vehicular Location-Based Metadata

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  • @ARTICLE{10.4108/eai.7-12-2017.153393,
        author={Zheng Tan and Cheng Wang and Xiaoling Fu and Jipeng Cui and Changjun Jiang and Weili Han},
        title={Re-identification of Vehicular Location-Based Metadata},
        journal={EAI Endorsed Transactions on Security and Safety},
        keywords={Privacy, VLBS, Re-identification, Uniqueness, Trajectories},
  • Zheng Tan
    Cheng Wang
    Xiaoling Fu
    Jipeng Cui
    Changjun Jiang
    Weili Han
    Year: 2017
    Re-identification of Vehicular Location-Based Metadata
    DOI: 10.4108/eai.7-12-2017.153393
Zheng Tan1,*, Cheng Wang1, Xiaoling Fu1, Jipeng Cui1, Changjun Jiang1, Weili Han2
  • 1: Tongji University, Shanghai 201804, China
  • 2: Software School, Fudan University, Shanghai 201203, China
*Contact email:


Amid the flourish of various data services, the privacy problems on metadata have received sufficient attention. Generally, the identity is the most sensitive attribute in metadata as identity is the key linking all attributes together. Thus, quite a few methods, such as dummy and k-anonymity, have been applied to actual applications to protect the identity . However, we still argue that the identity is very likely to be disclosed. In this paper, we study the re-identification problem in the seemingly privacy-preserving VLBS (Vehicular Location-Based Service). We find that the trajectories of vehicles are highly unique after studying 131 millions mobility traces of taxis. More specifically, the experiments demonstrate that only four spatio-temporal points are sufficient to uniquely re-identify the vehicle, achieving an accuracy of 95.35%. This indicates that there exists a high risk of re-identification in VLBS even identity has been protected by traditional methods.