Maze Routing: An Information Privacy-aware Secure Routing in Internet of Things for Smart Grid

Yao, Shiying; Yang, Xuanyu; Song, Zilong; Yang, Xiaolong; Duan, Dengwei; Yang, Haizhou

doi:10.1109/ccisp55629.2022.9974237

Cited by 1 publication

(1 citation statement)

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“…In traditional power systems, direct data transmission occurs between edge nodes and cloud servers. However, this method of data transmission is vulnerable to attacks by malicious third parties, resulting in data breaches that compromise the security of power data and lead to significant power incidents [8]. Moreover, future trends will require real-time data privacy and security to reduce the risk of data leaks and enhance the overall reliability of the power grid.…”

Section: Introductionmentioning

confidence: 99%

An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids

Pang,

Liang,

Zhang

et al. 2023

Applied Sciences

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In the context of rapid advancements in artificial intelligence (AI) technology, new technologies, such as federated learning and edge computing, have been widely applied in the power Internet of Things (PIoT). When compared to the traditional centralized training approach, conventional federated learning (FL) significantly enhances privacy protection. Nonetheless, the approach poses privacy concerns, such as inferring other users’ training data through the global model or user-transferred parameters. In light of these challenges, this research paper introduces a novel privacy-preserving data aggregation scheme for the smart grid, bolstered by an improved FL technique. The secure multi-party computation (SMC) and differential privacy (DP) are skillfully combined with FL to combat inference attacks during both the learning process and output inference stages, thus furnishing robust privacy assurances. Through this approach, a trusted third party can securely acquire model parameters from power data holders and securely update the global model in an aggregated way. Moreover, the proposed secure aggregation scheme, as demonstrated through security analysis, achieves secure and reliable data aggregation in the electric PIoT environment. Finally, the experimental analysis shows that the proposed scheme effectively performs federated learning tasks, achieving good model accuracy and shorter execution times.

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Section: Introductionmentioning

confidence: 99%