A new trend of using deep reinforcement learning for traffic light control has become a spotlight in the Intelligent Transportation System (ITS). Meanwhile, some promising accomplishments have also been made. However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters' privacy. Privacy leaks cause unnecessary trouble for users. For example, the personal interests and health information of the commuter can be inferred based on the driving route of the vehicle, or receive harassment advertisements for surrounding services, etc. In this paper, we propose a privacy-preserving intelligent traffic light control (PTLC) system with Deep Q-network. To achieve practicality and confidentiality simultaneously, a series of secure and efficient interactive protocols is designed depending on additive secret sharing to perform the DQN. Moreover, the security of PTLC is testified, meanwhile, the system effectiveness, and the overall efficiency of PTLC is demonstrated through theoretical analysis and simulation experiments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Privacy-preserving Intelligent Traffic Light Control


    Beteiligte:
    Ying, Zuobin (Autor:in) / Cao, Shuanglong (Autor:in) / Xu, Shengmin (Autor:in) / Liu, Ximeng (Autor:in) / Ma, Maode (Autor:in)


    Erscheinungsdatum :

    01.12.2020


    Format / Umfang :

    2334559 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    PrivacySignal: Privacy-Preserving Traffic Signal Control for Intelligent Transportation System

    Ying, Zuobin / Cao, Shuanglong / Liu, Ximeng et al. | IEEE | 2022



    Privacy-preserving cooperative services for Smart Traffic

    Florian, Martin | TIBKAT | 2016

    Freier Zugriff

    Privacy-Preserving Multi-period Traffic Prediction Model

    Sun, Shu / Zhu, Zheren / Zhang, Xinmin et al. | Springer Verlag | 2025


    Privacy-Preserving Traffic Flow Estimation for Road Networks

    Bentafat, Elmahdi / Rathore, M. Mazhar / Bakiras, Spiridon | IEEE | 2020