The rising connection of vehicles with the road infrastructure enables the creation of data-driven applications to offer drivers customized services. At the same time, these opportunities require innovative solutions to protect the drivers’ privacy in a complex environment like an Intelligent Transportation System (ITS). This need is even more relevant when data are used to retrieve personal behaviors or attitudes. In our work, we propose a privacy-preserving solution, called Private Driver DNA, which designs a possible architecture, allowing drivers of an ITS to receive customized services. The proposed solution is based on the concept of Driver DNA as characterization of driver’s driving style. To assure privacy, we perform the operations directly on sanitized data, using the Order Revealing Encryption (ORE) method. Besides, the proposed solution is integrated with ITS architecture defined in the European project E-Corridor. The result is an effective privacy-preserving architecture for ITS to offer customized products, which can be used to address drivers’ behaviors, for example, to environmental-friendly attitudes or a more safe driving style. We test Private Driver DNA using a synthetic dataset generated with the vehicle simulator CARLA. We compare ORE with another encryption method like Homomorphic Encryption (HE) and some other privacy-preserving schemas. Besides, we quantify privacy gain and data loss utility after the data sanitization process.


    Access

    Download


    Export, share and cite



    Title :

    A Privacy-Preserving Solution for Intelligent Transportation Systems: Private Driver DNA




    Publication date :

    2023-01-01


    Size :

    2780162 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Blockchain-Based Privacy-Preserving Authentication Model Intelligent Transportation Systems

    Qureshi, Kashif Naseer / Jeon, Gwanggil / Hassan, Mohammad Mehedi et al. | IEEE | 2023


    Location-Aware and Privacy-Preserving Data Cleaning for Intelligent Transportation

    Wang, Yuqing / Zhang, Junwei / Ma, Zhuo et al. | IEEE | 2024


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

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



    A Survey on Privacy-Preserving Electronic Toll Collection Schemes for Intelligent Transportation Systems

    Adavoudi Jolfaei, Amirhossein / Boualouache, Abdelwahab / Rupp, Andy et al. | IEEE | 2023