A Secure and Efficient Multi-Dimensional Perception Data Aggregation in Vehicular Ad Hoc Networks

Abstract

In vehicular ad hoc networks (VANETs), data aggregation is pivotal as it consolidates data from multiple vehicles for further analysis. Malicious users may launch attacks during the aggregation process to threaten the security and privacy of vehicles. Therefore, it is essential to ensure the security of vehicle data aggregation in vehicular networks. In order to deal with the security risks and challenges related to data aggregation in VANETs, this paper proposes a secure and efficient multi-dimensional perception data aggregation solution. The proposed solution integrates cloud computing with blockchain, presenting a blockchain-based data aggregation system for vehicular networks, enabling efficient and secure data collection and analysis tasks. This solution utilizes an enhanced Paillier cryptosystem to protect location privacy when aggregating sensor data. Additionally, it constructs multi-dimensional sensor data from different locations. A central control centre can fully recover and analyze the aggregated data results. The security analysis has demonstrated the security and effectiveness of the solution, while the performance evaluation has verified that the solution incurs low computational and communication overheads.

Keywords

• Data aggregation
• privacy protection
• blockchain
• vehicular ad-hoc networks
• security risks