Connected Vehicles Secure Data Sharing using Secure and Differential Privacy computation on multi-party

To ensure effective and secure transportation systems, networked automobiles need to share enormous amounts of data that safeguard users' privacy. Data leakage issues, large computing expense, and lack of differential privacy guarantees are some of the challenges with existing methods. The Privacy-Preserving Vehicular Data Exchange Framework (PPVDEF) to address the above issues. It integrates differential privacy (DP) with secure multi-party computation (SMPC) to render data exchange among infrastructure, cloud, and automobiles safe and speedy. While PPVDEF supports collective intelligence through privacy-conscious data analytics and aggregation, it ensures sensitive vehicle data remains confidential. The proposed methodology enhances security through unwanted access blocking and ensures privacy by adding controlled noise to keep personal data private. The study indicates that PPVDEF makes vehicle data analytics significantly more accurate, efficient, and scalable without compromising data privacy. The approach encourages smarter and safer transportation networks by providing a platform where connected cars can trust each other and their privacy can be ensured. The proposed Method achieves the data privacy by 97.6%, accuracy by 98.5% and scalability by 96.3%.