A Lightweight Privacy-Preserving Multi-Dimensional Data Aggregation Scheme for the Internet of Vehicles

The Internet of Vehicles (IoV) leverages wireless communication technologies to connect vehicles, traffic infrastructure, and cloud platforms, enabling real-time information exchange and improving traffic safety through collision warnings and cooperative perception. However, the information exchange process faces significant challenges, including risks to data privacy, malicious data injection, and high computational and communication overhead. To address the issues of excessive computational complexity and bandwidth consumption in existing multi-dimensional data aggregation schemes and aggregate signature verification, this paper proposes an efficient multi-dimensional data aggregation and verification scheme based on secret sharing and lightweight aggregate verification. During the data generation, the proposed scheme introduces blind factors to obfuscate raw data, thereby protecting sensitive information. Simultaneously, a secret sharing mechanism divides data into multiple shares, enhancing data security. Moreover, lightweight aggregate signature techniques significantly reduce the computational and communication overhead in the integrity verification process. In the data recovery phase, blind factors are directly removed to restore the data without decryption, further optimizing computational efficiency. Performance evaluation results demonstrate that the proposed scheme outperforms existing approaches regarding computational and communication costs, showcasing higher efficiency and broader applicability.