Enabling Efficient and Malicious Secure Data Aggregation in Smart Grid With False Data Detection

As the next-generation power grid, the smart grid has significantly improved dependability, flexibility, and efficiency compared with the traditional power grid. However, due to increasingly diverse application requirements, it faces challenges on balancing data privacy, efficiency, and robustness....

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Bibliographic Details
Published in:IEEE transactions on smart grid 2024-03, Vol.15 (2), p.2203-2213
Main Authors: Pang, Haolin, He, Kai, Fu, Youcai, Liu, Jia-Nan, Liu, Xueqiao, Tan, Wuzheng
Format: Article
Language:English
Subjects:
Cloud computing
Computational modeling
Data aggregation
Data management
Data privacy
Edge computing
Electric power grids
Electricity distribution
false data detection
malicious secure
Privacy
privacy-preserving
Robustness
Smart grid
Smart grids
Smart meters
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Summary:As the next-generation power grid, the smart grid has significantly improved dependability, flexibility, and efficiency compared with the traditional power grid. However, due to increasingly diverse application requirements, it faces challenges on balancing data privacy, efficiency, and robustness. In this paper, we present a fog computing-based smart grid model. In addition, based on the proposed model, we construct an efficient and privacy-preserving scheme that supports malicious secure smart grid usage data aggregation communication. To our best knowledge, this is the first concrete smart grid solution that concurrently achieves secure aggregation communication, data privacy, and data robustness (e.g., false data detection). Specifically, benefiting from Boolean/Arithmetic secret-sharing methods, our proposed scheme allows home users to report their electricity usage data to the cloud and fogs securely. Besides, a false data detection protocol is proposed to resist false data injection attacks launched by malicious home users. Theoretical analysis and experimental implementation show that our scheme efficiently achieves data security, anonymity, and robustness.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2023.3316730