计算机工程2025，Vol.51Issue(12)：68-81,14.DOI:10.19678/j.issn.1000-3428.0069639
基于同态加密的隐私保护逻辑回归模型训练方案
Privacy-Preserving Logistic Regression Model Training Scheme Based on Homomorphic Encryption
摘要
Abstract
Logistic regression is widely used in big data for predicting the probability of event occurrence.This study focuses on scenarios involving two parties and data being horizontally distributed.Based on Cheon-Kim-Kim-Song(CKKS)encryption scheme,a logistic regression model training scheme is designed.This scheme reduces the number of iterations in the training process using Newton's second-order approximation method.It employs the conjugate gradient method to solve Newton's second-order approximation and introduces a small amount of interaction,thereby significantly reducing the computational overhead of the ciphertext domain.Additionally,a new encoding method is used to reduce the number of ciphertext multiplications and communication overhead.Experimental results show that,for most datasets,using the Newton's second-order approximation method to set the number of iterations to less than three can achieve an accuracy comparable to that of the existing privacy protection schemes comprising five to seven iterations.For sample datasets with 60 and 112 dimension,existing schemes require 90 and 165 s,respectively,to complete five iterations,whereas the proposed scheme requires only 8 and 27 s.Moreover,the communication overhead is reduced to half that of the original scheme,requiring only 30.8 and 62.7 Mb to complete the training.关键词
隐私保护/牛顿-共轭梯度法/逻辑回归/同态加密/CKKS方案Key words
privacy protection/Newton-conjugate gradient method/logistic regression/homomorphic encryption/Cheon-Kim-Kim-Song(CKKS)scheme分类
信息技术与安全科学引用本文复制引用
MIAO Weijie,WU Wenyuan..基于同态加密的隐私保护逻辑回归模型训练方案[J].计算机工程,2025,51(12):68-81,14.基金项目
国家重点研发专项(2020YFA0712300) （2020YFA0712300）
重庆市在渝院士牵头科技创新引导专项(2022YSZX-JCX0011CSTB,cstc2021yszx-jcyjX0004,CSTB2023YSZX-JCX0008,cstc2021jcyj-msxmX0821). （2022YSZX-JCX0011CSTB,cstc2021yszx-jcyjX0004,CSTB2023YSZX-JCX0008,cstc2021jcyj-msxmX0821）
