Quantum-Inspired Metaheuristic Algorithms for Trust-Based Privacy Agreements and Secure Access in the Internet of Vehicles

Internet of Vehicles (IoV) is a new type of network that enables communication among vehicles, infrastructure, and users, providing better traffic systems. When exchanging information, the IoV systems face challenges such as data privacy, access control, and trust management issues. The security mechanisms are utilized to address privacy and trust issues. However, existing solutions still face adaptability, scalability, and efficiency issues when handling the dynamic requirements of IoV ecosystems. This study addressed security issues by designing the Quantum-Inspired Metaheuristic Framework (QIMF), which integrates a quantum optimization algorithm and adaptive privacy agreements to enhance security between IoV entities. During the analysis, the trust score is evaluated for all entities, which helps to minimize unauthorized access and manage trustworthiness using the quantum-inspired approach while exchanging information. The private agreement is assessed via the computed trust score, which improves the policy’s construction. According to the policy, secure access control is achieved by integrating the quantum superposition and tunnelling features to ensure scalable and secure transactions. The discussed system is implemented using the Python tool, and the CICIoV2024 dataset is used to evaluate the discussed system’s efficiency. The QIMF achieves 99.2% trust accuracy with a minimum false acceptance and rejection rate. The integrated approach ensures scalable and secure solutions for improving vehicular networks.