Optimized Fully Homomorphic Encryption Strategy Based on Residue Number System

With the increasing demand for data security computing, the BFV algorithm has become a research hotspot due to its relatively high efficiency. However, its performance bottlenecks limit its widespread deployment in practical applications when dealing with large-scale data and complex computations. In order to address this challenge, this study proposes a fully homomorphic encryption optimization strategy based on the Residue Number System (RNS). The study first conducts an in-depth analysis of the performance bottlenecks of the BFV algorithm, then elaborates on the theoretical foundation and implementation methods of the RNS optimization strategy. By constructing an experimental framework, we compare traditional BFV algorithms with RNS-optimized versions. Further analysis reveals the significant advantages of RNS in reducing modulus operation complexity, enhancing parallel processing capabilities, and controlling data expansion. Despite the implementation complexities, this optimization strategy holds promise for improving algorithm performance. The findings of this study provide new perspectives for optimizing fully homomorphic encryption algorithms and lay the foundation for future applications in areas such as cloud computing and big data analytics.