An Innovative Anti-Tampering Encryption Method for Enhancing Privacy Protection in Power Big Data

Abstract

Power big data encompasses vast amounts of private information. Traditional encryption algorithms often exhibit longer computation times and greater resource demands when handling large-scale data. This not only heightens the risk of private information decryption but also diminishes encryption efficiency. To address these challenges, we propose a novel tamper-proof encryption method for power big data based on national security algorithms. Initially, we conduct an analysis of the composition, operational principles, and advantages of various national security algorithms. Subsequently, we select the Advanced Encryption Standard (AES) algorithm and enhance its performance through expansion and optimization. Finally, the improved AES algorithm is employed to achieve tamper-proof encryption of power big data privacy information, utilizing operations such as round transformation, byte substitution, and row matrix manipulation. Experimental results demonstrate that the proposed encryption method significantly reduces the likelihood of private information decryption while enhancing encryption efficiency. These findings underscore the effectiveness of our approach in achieving robust tamper-proof encryption for power big data.