Secure and Privacy-Aware Traffic Management Services via Autonomous Vehicles

Advancements in technology have accelerated the development of smart cities, with the Internet of Vehicles (IoV) playing a crucial role in integrating urban infrastructure and vehicles to enhance transportation safety and efficiency. This integration enables wireless communication and the exchange of critical traffic data. However, it also introduces significant security and privacy concerns, as unauthorized access has become a primary threat. Hackers can exploit system vulnerabilities to compromise the integrity and confidentiality of sensitive data, while privacy risks such as unauthorized tracking, identity exposure, data misuse, and information leakage pose serious challenges. This thesis addresses these concerns and proposes secure and privacy-preserving schemes specifically designed for autonomous vehicles (AVs) in the IoV and Traffic Management Systems (TMS) ecosystems, focusing on secure authentication and encrypted route reporting. To mitigate these security and privacy risks, the proposed approaches ensure the utilization of AVs in the TMS. This is achieved through a two-step process. The first step involves securely collecting AVs’ routing data through an authentication scheme that employs a privacy-preserving authentication mechanism based on the k-Nearest Neighbors (KNN) encryption technique. This method is further reinforced by incorporating a Physical Unclonable Functions (PUFs) design to generate random cryptographic keys, providing robust protection against cyber threats such as identity fraud and man-in-the-middle attacks. Given the extensive data processing demands associated with authentication, the scheme is implemented with hardware acceleration on an FPGA board, ensuring reduced computational burden and optimized resource utilization compared to existing methods. The second step focuses on aggregating encrypted data securely using KNN and bilinear pairing, enabling privacy-preserving AV route reporting for traffic management without exposing the individual AV’s routing data. This approach employs an aggregation-over-encrypted-data technique, where AV data is securely collected through authentication and aggregated using KNN and bilinear pairing techniques. A KNN-based aggregation scheme over encrypted data is introduced to minimize communication and computation overhead compared with other aggregation schemes. Also, group signatures have been utilized to allow AV membership verification while maintaining anonymity. Additionally, a secure aggregation scheme leveraging bilinear pairing techniques is incorporated to enhance privacy, making it particularly effective for small urban environments to overcome the shortcomings of the kNN-based aggregation scheme. The main design objectives of these schemes are to ensure scalability, efficiency, and strong privacy guarantees by maintaining data confidentiality for AVs. Security and privacy analyses confirm the effectiveness of the proposed methods, while performance evaluations highlight minimal communication and computational overhead alongside optimized hardware resource utilization compared with other existing schemes. These advanced privacy-preserving techniques contribute to the realization of intelligent urban management, strengthening transportation safety and efficiency in the IoV while safeguarding user privacy.