EVPDA: An efficient and verifiable privacy-preserving data aggregation scheme for Vehicle-to-Grid networks

Vehicle-to-Grid (V2G) networks critically depend on efficient data aggregation to maintain grid stability. However, the aggregation process inevitably raises significant privacy concerns and security risks. Existing approaches typically suffer from heavy computational overhead or lack reliable mechanisms to verify the correctness of aggregated results in the presence of potentially malicious aggregators, which limits their practical deployment. To overcome these challenges, we present EVPDA, a verifiable and privacy-preserving data aggregation scheme tailored for V2G networks. EVPDA leverages a lightweight symmetric masking technique to achieve high computational efficiency, while introducing a verifiable aggregation mechanism using Multi-Key Homomorphic MACs. This design enables the utility to efficiently verify both the authenticity and integrity of aggregated data even if the aggregator cannot be fully trusted. Rigorous security analysis shows that EVPDA provides privacy protection under the stated threat model and resilience against forgery and relevant collusion cases. Experimental evaluations further show that EVPDA achieves favorable communication and computation efficiency compared with the selected representative schemes, suggesting its practical potential for large-scale V2G deployments.