Blockchain-Enabled Secure and Privacy-Preserving Data Aggregation for Fog-Based ITS

【Author】 Chen, Siguang; Yang, Li; Shi, Yanhang; Wang, Qian

【Source】CMC-COMPUTERS MATERIALS & CONTINUA

【影响因子】3.860

【Abstract】As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system col-lects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things (IoT) environment. To address above problems, this paper proposes a blockchain-enabled secure and privacy-preserving data aggregation scheme for fog-based ITS. First, a fog computing and blockchain co-aware aggrega-tion framework of power injection data is designed, which provides strong support for ITS to achieve secure and efficient power injection. Second, Paillier homomorphic encryption, the batch aggregation signature mechanism and a Bloom filter are effectively integrated with efficient aggregation of power injection data with security and privacy guarantees. In addition, the fine-grained homomorphic aggregation is designed for power injection data generated by all EVs, which provides solid data support for accurate power dispatching and supply management in ITS. Experiments show that the total computational cost is significantly reduced in the proposed scheme while providing security and privacy guarantees. The proposed scheme is more suitable for ITS with latency-sensitive applications and is also adapted to deploying devices with limited resources.

【Keywords】Blockchain; fog computing; security; privacy -preserving; ITS

【发表时间】2023

【收录时间】2023-05-30

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-交通领域