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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

by Congcong Wang1, Chen Wang2,3,*, Wenying Zheng4,*, Wei Gu5

1 School of Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, China
3 State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China
4 School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, 310018, China
5 School of Computer Science (School of Cyber Science and Engineering), Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Authors: Chen Wang. Email: email; Wenying Zheng. Email: email

(This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)

Computers, Materials & Continua 2025, 82(1), 799-816. https://doi.org/10.32604/cmc.2024.057975

Abstract

As smart grid technology rapidly advances, the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection. Current research emphasizes data security and user privacy concerns within smart grids. However, existing methods struggle with efficiency and security when processing large-scale data. Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge. This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities. The approach optimizes data preprocessing, integrates Long Short-Term Memory (LSTM) networks for handling time-series data, and employs homomorphic encryption to safeguard user privacy. It also explores the application of Boneh Lynn Shacham (BLS) signatures for user authentication. The proposed scheme’s efficiency, security, and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

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Cite This Article

APA Style
Wang, C., Wang, C., Zheng, W., Gu, W. (2025). Ai-enhanced secure data aggregation for smart grids with privacy preservation. Computers, Materials & Continua, 82(1), 799-816. https://doi.org/10.32604/cmc.2024.057975
Vancouver Style
Wang C, Wang C, Zheng W, Gu W. Ai-enhanced secure data aggregation for smart grids with privacy preservation. Comput Mater Contin. 2025;82(1):799-816 https://doi.org/10.32604/cmc.2024.057975
IEEE Style
C. Wang, C. Wang, W. Zheng, and W. Gu, “AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation,” Comput. Mater. Contin., vol. 82, no. 1, pp. 799-816, 2025. https://doi.org/10.32604/cmc.2024.057975



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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