密码学报（中英文）2026，Vol.13Issue(1)：28-42,15.DOI:10.13868/j.cnki.jcr.000836
机器学习中核函数的隐私保护计算方法及应用
Privacy-Preserving Kernel Function Evaluation in Machine Learning and Its Application
摘要
Abstract
The kernel function maps data into a higher-dimensional space to address linear insepara-bility by quantifying similarity between cross-domain samples.However,its conventional computation on plaintext involving multi-party data interaction poses significant privacy leakage risks.This study proposes a privacy-preserving kernel function computation framework under the semi-honest model.First,three interactive sub-protocols are designed based on Paillier homomorphic encryption and ran-dom perturbation factors:secure inner product computation,secure power function computation,and secure Euclidean distance computation.By partitioning the plaintext space into congruence classes of positive/negative numbers and introducing floating-point scaling factors,compatibility issues of traditional encryption algorithms with real-world datasets are resolved.A nonlinear computation framework is constructed using interactive protocols,enabling secure computation of complex kernel functions through two-party computation and random perturbation techniques combined with Taylor polynomial approximation,while relying solely on additive homomorphic encryption.The framework supports secure computation of linear kernels,polynomial kernels,and Gaussian kernels within a single cryptographic system.The correctness,security,and computational complexity of the scheme are ana-lyzed,and its application scenarios are discussed.Experimental results show that the proposed scheme achieves the privacy protection goal effectively while maintaining the accuracy of kernel function model,and has the advantages of low computational complexity and low time overhead.关键词
线性可分/线性核/多项式核/高斯核/隐私保护Key words
linearly separable/linear kernel/polynomial kernel/gaussian kernel/privacy protection分类
信息技术与安全科学引用本文复制引用
张明武,黄子麒,王玉珠..机器学习中核函数的隐私保护计算方法及应用[J].密码学报（中英文）,2026,13(1):28-42,15.基金项目
国家自然科学基金(62472150,62072134) （62472150,62072134）
湖北省重大研究计划(2023BAA027) （2023BAA027）
湖北省重点研发计划(2021BEA163)National Natural Science Foundation of China(62472150,62072134) （2021BEA163）
Major Research Plan of Hubei Provience(2023BAA027) （2023BAA027）
Key Research and Development Program of Hubei Province(2021BEA163) （2021BEA163）
