With the advances of Internet of Things (IoT) solutions in intelligent transportation systems, collected vehicle data can produce insights on emerging vehicular phenomenon, and further contribute to the further improvement of innovative and efficient vehicular systems. Particularly, by leveraging data collected from vehicle sensors and maintenance models constructed from operation and repair history, predictive maintenance aims to detect the anomalies of vehicles and provide early warnings before the occurrence of failure. However, privacy preservation still remains as one of the top concerns for vehicle owners in predictive maintenance, as the sensory data could potentially violate their location and identity privacy. To address this challenge, in this article, we propose a privacy-preserving and verifiable continuous data collection scheme with the intent of predictive maintenance in vehicular fog, which gathers and organizes the sensor data of each individual vehicle on a sliding window basis. Specifically, our proposed scheme exploits the homomorphic Paillier cryptosystem and truncated $\alpha $ -geometric technique to protect the content of each individual piece of sensory data. Meanwhile, our proposed scheme also aggregates and authenticates the collected sensory data reports on a time-series sliding window basis, which achieves the continuous observation of the recently collected vehicular sensory data. Detailed security analysis is carried out to demonstrate the security properties of our proposed scheme, including confidentiality, authentication and privacy preservation. In performance evaluations, we also compare our proposed scheme with a traditional scheme, and our scheme shows great improvement in terms of communication and computation overheads. Furthermore, to show the feasibility of our proposed scheme, we also compare and discuss the expected squared error introduced by the differential privacy mechanism.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Privacy-Preserving Continuous Data Collection for Predictive Maintenance in Vehicular Fog-Cloud


    Contributors:
    Kong, Qinglei (author) / Lu, Rongxing (author) / Yin, Feng (author) / Cui, Shuguang (author)


    Publication date :

    2021-08-01


    Size :

    1178905 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Cloud-Based Privacy-Preserving Parking Navigation Through Vehicular Communications

    Ni, Jianbing / Zhang, Kuan / Lin, Xiaodong et al. | Springer Verlag | 2017


    Auditable and Privacy-Preserving Authentication in Vehicular Networks

    Kim, Sung Hoon / Kim, Bum Han / Kim, Young Ki et al. | IEEE | 2008


    Vehicular Cloud data collection for Intelligent Transportation Systems

    Chaqfeh, Moumena / Mohamed, Nader / Jawhar, Imad et al. | IEEE | 2016


    Preserving Data Privacy in Cloud using Homomorphic Encryption

    Murthy, Shrujana / C.R., Kavitha | IEEE | 2019


    Preserving Location-Privacy in Vehicular Networks via Reinforcement Learning

    Berri, Sara / Zhang, Jun / Bensaou, Brahim et al. | IEEE | 2022