信息安全研究2026，Vol.12Issue(5)：439-444,6.DOI:10.12379/j.issn.2096-1057.2026.05.06
全同态加密CNN推理的内存与噪声协同优化方法
Memory and Noise Co-optimization Method for Fully Homomorphic Encryption CNN Inference
摘要
Abstract
To address the challenges of high memory consumption,low computational efficiency,and homomorphic noise accumulation in fully homomorphic encryption(FHE)for privacy-preserving inference in convolutional neural network(CNN),this paper proposes a collaborative optimization framework.The framework introduces a hierarchical memory scheduling strategy,which employs a dynamic key loading mechanism and an adaptive compression technique for polynomial ring slot numbers(reducing available slots exponentially based on network depth),thereby significantly decreasing memory usage.Additionally,a noise suppression residual module is developed,incorporating a noise propagation dynamics model to design a real-time noise monitoring-based on-demand bootstrapping trigger mechanism,which reduces bootstrapping frequency and enhances inference efficiency.Experimental results on the CIFAR-10 dataset demonstrate that this framework enables homomorphic encrypted inference of ResNet-20 in approximately 500 s with only 20 GB of memory,achieving a 3.5×improvement in inference efficiency and a 94％reduction in memory consumption compared to existing CKKS-based solutions(2 271 s/384 GB).This framework provides a novel technical paradigm for privacy-preserving machine learning in resource-constrained scenarios.关键词
隐私保护机器学习/全同态加密/噪声抑制/残差网络/层次化内存调度Key words
privacy-preserving machine learning/fully homomorphic encryption/noise suppression/residual network/hierarchical memory scheduling分类
信息技术与安全科学引用本文复制引用
李开颜,贾洪勇,曾俊杰,张建辉..全同态加密CNN推理的内存与噪声协同优化方法[J].信息安全研究,2026,12(5):439-444,6.基金项目
河南省重点研发专项(231111211900) （231111211900）
嵩山实验室资助项目(221100210900) （221100210900）
