通信学报2024，Vol.45Issue(z1)：12-23,12.DOI:10.11959/j.issn.1000-436x.2024216
基于同态密文转换的隐私保护卷积神经网络推理方案
Privacy-preserving convolutional neural network inference scheme based on homomorphic ciphertext transformation
摘要
Abstract
To solve the problems of frequent interaction and low prediction accuracy of existing privacy-protected convo-lutional neural networks,a homomorphic friendly non-interactive privacy-protected convolutional neural network infer-ence scheme was proposed via homomorphic ciphertext transformation.Utilizing the Pegasus framework,CKKS(Cheon-Kim-Kim-Song)ciphertext was used to parallelize convolution operations in convolution layer.In the activation layer and pooling layer,LWE ciphertext and LUT(look-up table)technology were used to calculate the activation func-tion,maximum pooling and global pooling.Using the ciphertext conversion technology provided by the Pegasus frame-work,the conversion between different forms of homomorphic ciphertext is realized.Theoretical analysis and experimen-tal results show that the proposed scheme can ensure data security,and has higher inference accuracy and lower calcula-tion and communication overhead.关键词
隐私保护/卷积神经网络/同态加密/密文转换Key words
privacy-preserving/convolutional neural network/homomorphic encryption/ciphertext transformation分类
信息技术与安全科学引用本文复制引用
李瑞琪,易琴,黄艺璇,贾春福..基于同态密文转换的隐私保护卷积神经网络推理方案[J].通信学报,2024,45(z1):12-23,12.基金项目
天津市教委科研计划基金资助项目(No.2022KJ066)The Education Committee Research Program of Tianjin(No.2022KJ066) （No.2022KJ066）
