Traffic congestions cause not only the time- consuming and frustrating experiences to drivers, but also other critical problems, such as fuel waste, air pollution and accidents. Real-time traffic information exchange can avoid vehicles being congested on roads. However, when the drivers are acquiring the traffic information, their privacy is inevitable to be disclosed. To preserve the driver's privacy, in this paper, we propose a privacy-preserving real-time navigation system (PRIN) using vehicular crowdsourcing. In PRIN, the RSUs cooperatively find an optimal path for the querying vehicle to the destination according to the real-time traffic information crowdsourced by the vehicles in their coverage areas. The querying vehicle retrieves the navigation result from each RSU successively when entering its coverage area, and follows the proper driving route to the next RSU, until reaching its destination. During these querying, crowdsourcing and retrieving processes, the driver's personal information, such as location, identity, is protected from being disclosed to attackers. In addition, a trusted authority can trace the drivers' identities if they upload false traffic information. Finally, we discuss the properties of conditional privacy preservation and demonstrate the efficiency of PRIN.


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    Titel :

    Privacy-Preserving Real-Time Navigation System Using Vehicular Crowdsourcing


    Beteiligte:
    Ni, Jianbing (Autor:in) / Lin, Xiaodong (Autor:in) / Zhang, Kuan (Autor:in) / Shen, Xuemin (Autor:in)


    Erscheinungsdatum :

    2016-09-01


    Format / Umfang :

    333648 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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