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Open Access

ARTICLE

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

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 Author: 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 https://doi.org/10.32604/cmc.2024.057975

Received 01 September 2024; Accepted 23 October 2024; Published online 13 November 2024

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.

Keywords

Smart grid; data security; privacy protection; artificial intelligence; data aggregation

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