An Efficient Privacy-Preserving Data Aggregation Protocol for Edge Computing Assisted VANETs

Abstract

Edge computing empowers Vehicular Ad-hoc Networks (VANETs) to perform local computations on data gathered by vehicles for decision-making. Privacy-preserving data aggregation is essential for reliable decisions and timely responses. This paper proposes a privacy-preserving data aggregation protocol using homomorphic signcryption for edge-enabled VANETs. The proposed protocol provides essential security attributes, including privacy protection, authentication, and data integrity. Additionally, it enables edge nodes to perform operations on encrypted data. A comparative analysis with state-of-the-art existing schemes is presented. The analysis shows that the proposed scheme reduces computation cost from 72% to 98% at the vehicular, edge and cloud servers and 54% to 75% communication overhead in the registration phase. In the data upload request phase, the proposed scheme reduces computation cost from 72% to 98% at vehicular, edge and cloud servers and communication overhead from 16% to 36%. While in the encrypted data generation/aggregation/decryption phase, the proposed protocol reduces computation cost from 25% to 97% at vehicular, edge and cloud servers and communication overhead from 6% to 89%. The proposed scheme exhibits lower implementation and memory consumption, making it an attractive solution for resource-constrained environments.