The leakage of driving positions or traces poses a serious privacy threat upon the users in the vehicular ad-hoc networks (VANETs). A series of pseudonym changing approaches have been proposed to achieve unlinkability between the users’ identities and their driving information. To investigate the effectiveness of the changing strategies on user anonymity, it is important to stand at the side of an adversary to implement posterior linking between different pseudonyms. In this paper, we remove the assumption of motion models commonly used in existing works, and propose a novel pseudonym linking scheme by focusing on the prediction of acceleration and direction angle. Further, we plug several side information (e.g., road structure, traffic signal) into the proposed scheme to improve the linking performance. Finally, based on five representative pseudonym changing strategies, extensive experiments are conducted to evaluate the performance of the proposed linking scheme. The experimental results show that the side-information assisted pseudonym linking scheme achieves success rates of over 74%.


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    Title :

    A Novel Pseudonym Linking Scheme for Privacy Inference in VANETs


    Contributors:
    Zhang, Rui (author) / Wang, Xin (author) / Cheng, Peng (author) / Chen, Jiming (author)


    Publication date :

    2020-05-01


    Size :

    188249 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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