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

• 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; Use statistics
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2023.3316730

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.