Multi-Functional Aggregation Strategy for Multi-Source Heterogeneous Data of Vehicle-Network Interaction Considering Privacy Protection

In order to realize vehicle to grid (V2G) network data aggregation with fault tolerance and multiple aggregation functions, this paper proposes a multi-source heterogeneous data aggregation strategy for vehicle-network interaction considering privacy protection. Firstly, a privacy-preserving data aggregation scheme is designed for V2G networks by integrating fully homomorphic encryption and secret sharing technology. Secondly, the fog calculation is introduced to unload the calculation pressure of the control center. It greatly reduces the computational overhead of the control center in the process of V2G network data aggregation. Then, the alliance chain is introduced to solve the dependence problem on trusted third parties, and the storage structure of transactions and blocks is adjusted to reduce communication and storage overhead. Finally, security analysis shows that the proposed method reduces the computational overhead of the control center by 64.3%, and reduces the total communication overhead of the charging pile and the system by 23.4% and 17.2%, respectively.