Enhancing Data Privacy in Vehicular Cloud Networks: Leveraging Differential Privacy and Homomorphic Encryption

As Vehicular Cloud Networks (VCNs) become increasingly integral to the future of intelligent transportation systems, safeguarding the privacy of data generated by connected vehicles is paramount. This paper introduces a comprehensive framework that combines Differential Privacy and Homomorphic Encryption to address these privacy concerns. Differential Privacy is utilized to anonymize data at the source, ensuring that individual vehicle data remains confidential while allowing meaningful analytics. This technique mitigates the risk of re-identification by introducing controlled noise, balancing the trade-off between data utility and privacy. Simultaneously, Homomorphic Encryption enables secure computations on encrypted data within the cloud environment, ensuring that sensitive information remains protected throughout its lifecycle, even during complex processing tasks. The proposed framework was evaluated using simulated vehicular data, demonstrating a 25% improvement in privacy preservation over traditional kanonymity methods while maintaining a comparable level of data utility. Additionally, the framework's use of Homomorphic Encryption allowed for secure data aggregation in the cloud without exposing plaintext data, outperforming standard encryption methods in terms of both security and computational efficiency. These findings highlight the framework's effectiveness in enhancing end-to-end data protection and supporting the development of privacy-centric applications in vehicular networks, paving the way for safer and more secure vehicular communications. This work underscores the importance of advanced cryptographic methods in fostering trust and facilitating the adoption of nextgeneration transportation systems.