基于云隐私保护的支持向量机训练外包
Support Vector Machine Training Outsourcing Based on Cloud Privacy-Preserving
摘要: 支持向量机(Support Vector Machine, SVM)是一种监督式学习机器学习分类算法，自出现以来，已经成为应用最广泛的分类算法。本文探讨了云环境下隐私保护的SVM训练问题，并提出了一种新的隐私保护的SVM外包训练(PPOSVM)协议。与已有的方案相比，除了隐藏数据样本的隐私性，新协议首次考虑了样本数据与标签对应关系的隐私性，隐藏了密文数据的访问模式。同时，采用豪斯霍尔德变换以及置换矩阵进行加密操作，使得新协议具有很高的效率。严格论证了协议的输入输出隐私性以及效率，同时，通过广泛的实验分析验证了所提协议的实际性能，进一步证实了理论分析。
Abstract: Support Vector Machine (SVM) is a supervised learning machine learning classification algorithm. Since its emergence, it has become the most widely used classification algorithm. This paper discusses the problem of SVM training for privacy-preserving in the cloud environment, and proposes a new privacy-preserving SVM outsourcing training protocol. Compared with the existing schemes, in addition to hiding the privacy of data samples, the new protocol considers the privacy of the corresponding relationship between sample data and labels for the first time, and hides the access mode of ciphertext data. At the same time, the use of Householder transformation and permutation ma-trix for encryption operation makes the new protocol highly efficient. The input and output privacy and efficiency of the protocol are strictly demonstrated. At the same time, the actual performance of the proposed protocol is verified through extensive experimental analysis, which further confirms the theoretical analysis.
文章引用：邵宇航. 基于云隐私保护的支持向量机训练外包[J]. 计算机科学与应用, 2021, 11(12): 3038-3050. https://doi.org/10.12677/CSA.2021.1112307

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