Recently, vehicular crowdsensing networks have attracted much attention because of their ability to provide efficient and convenient information services for the Internet of Vehicles. How to achieve on-demand message authentication and provide privacy protection of sensing vehicles are challenging in accurate sensing tasks. We propose a secure vehicular crowdsensing scheme based on multi-authority attribute-based signature (TRAMS), which allows the publisher to flexibly customize a fine-grained policy that the potential participants must satisfy and uses attribute-based signature to authenticate sensed messages while protecting the privacy of the sensing vehicle. Also, we propose a multi-authority key management scheme, which can improve vehicle-based sensing efficiency in the Internet of Vehicles. Performance analysis shows that our scheme can not only achieve massage authentication while protecting the privacy of the sensing vehicle, but also ensure fine-grained message authentication to meet the expectation of the publisher on demand. And compared with the single-authority schemes in vehicular communication, our multi-authority TRAMS can achieve efficient message authentication for vehicular crowdsensing applications which require timely task feedback.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    TRAMS: A Secure Vehicular Crowdsensing Scheme Based on Multi-Authority Attribute-Based Signature


    Contributors:
    Liu, Xuejiao (author) / Chen, Wei (author) / Xia, Yingjie (author) / Shen, Renhao (author)


    Publication date :

    2022-08-01


    Size :

    2107460 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Hybrid Recruitment Scheme Based on Deep Learning in Vehicular Crowdsensing

    Fu, Yanming / Qin, Xiaoqiong / Zhang, Xian et al. | IEEE | 2023


    Sensing Vehicle Selection Scheme Optimization in Vehicular Crowdsensing

    Yu, Haiyang / Liu, Chenyang / Yang, Yang et al. | ASCE | 2020


    Secure Vehicular Crowdsensing and Malicious Vehicles Detection in VANETs

    Xia, Yingjie / Liu, Xuejiao / Wu, Huihui et al. | Springer Verlag | 2025



    Sensing Vehicle Selection Scheme Based on Road Importance in Vehicular Crowdsensing

    Yu, Haiyang / Liu, Chenyang / Liu, Shuai et al. | ASCE | 2019