In the future of intelligent transport systems, vehicles will communicate with other vehicles and infrastructure. Through this communication, vehicles can cooperate in traffic situations such as intersections and highway merging. This communication can also facilitate sharing of information about other vehicles. ETSI provides a protocol for this communication, the ETSI ITS-G5 protocol. With this protocol, these vehicular ad-hoc networks (VANETs) can decrease traffic congestion, road accidents and travelling time. However, communication also comes with a risk. Communication requires trust, otherwise maliciously intended actors can spread false information which if acted upon might lead to traffic jams, traffic crashes and potential deaths. As a countermeasure ETSI has an architecture for reporting suspect abnormal behaviour: misbehaviour reports. Misbehaviour reports are sent to a centralized authority able to discontinue vehicles’ involvement in the VANET. ETSI does not include a warning system between vehicles until the misbehaviour report has been analyzed by the authority. After that, all vehicles will have to experience the abnormal behaviour first-hand without warning. A vehicle-to-vehicle warning system would help vehicles heed extra caution when meeting these misbehaving vehicles, decreasing the risk of interacting with them. The challenges of such a system are protecting the reporter’s and reported’s integrity, bad actors sending false reports and the system being quicker than the authority. To protect the integrity of vehicles in the VANET, this thesis proposes proving the misbehaviour using non-interactive zero-knowledge proofs, allowing to share of evidence without revealing more than necessary to prove the abnormal behaviour.


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    Title :

    Peer-to-peer misbehaviour reporting using non-interactive zero-knowledge proofs in ETSI ITS-G5


    Contributors:
    Nee, Lukas (author)

    Publication date :

    2022-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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