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

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.