Internet of Vehicles (IoV) is a variant of Vehicular Ad-Hoc Network (VANET), it is being developed as an important communication way between vehicles. However, when some traffic messages are collected in IoV, these messages are usually linked to specific identifiable information. Therefore, there exists the privacy-preserving problem of vehicular identities in IoV. On the other hand, if the private information of vehicles is fully protected in IoV, then the true vehicular identities cannot be determined because the transferred messages are not related with the specific information of vehicles. Then it will also lead to more security problems in IoV. Additionally, fog computing seamlessly integrates heterogeneous computing resources widely distributed in edge networks and then provides stronger computing services for users. Therefore, in this paper we propose a decentralized traceable privacy-preserving scheme for vehicular identity in fog computing-based IoV, where our scheme uses multiple fog servers to trace the specific identity and most likely trajectory of a vehicle by the collected data under certain conditions. In our scheme, the true identity of a vehicle is hidden to some related parameters generated by the certificate authority; further the secret sharing scheme is used to hide and trace the true identity of a vehicle. We construct a voting mechanism to generate the most reliable fog server, which is able to calculate the true identity and corresponding trajectory of a vehicle by reconstructing the polynomial based on the secret sharing scheme. Additionally, we analyze that our scheme can satisfy the security requirements, and formally prove that the data collection procedure is secure under the real-or-random model. Also, the experimental results show our scheme is efficient in IoV.


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    Titel :

    Multi-Fogs-Based Traceable Privacy-Preserving Scheme for Vehicular Identity in Internet of Vehicles


    Beteiligte:
    Gu, Ke (Autor:in) / Wang, Keming (Autor:in) / Li, Xiong (Autor:in) / Jia, Weijia (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2022


    Format / Umfang :

    8022376 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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