Machine learning classification is an useful tool for trend prediction by analyzing big data. As supporting homomorphic operations over encrypted data without decryption, fully homomorphic encryption (FHE) contributes to machine learning classification without leaking user privacy, especially in the outsouring scenario. In this paper, we propose an improved FHE scheme based on HElib, which is a FHE library implemented based on Brakerski's FHE scheme. Our improvement focuses on two aspects. On the one hand, we first use the relinearization technique to reduce the ciphertext size, and then the modulus switching technique is used to reduce the modulus and decryption noise. On the other hand, we need no relinearization and modulus switching if there is additive homomorphic or no homomorphic operation in the multiplicative ciphertext's next homomorphic operation. Homomorphic comparison protocol, private hyperplane decision-based classification and private Naïve Bayes classification are implemented by additive homomorphic and multiplicative homomorphic first. In our homomorphic comparison protocol, the number of interactions is reduced from 3 to 1. We choose the proposed FHE scheme to implement private decision tree classification. Simulation results show that the efficiency of our FHE scheme and implementation of private decision tree classification are more efficient than other two schemes.

中文翻译：

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基于全同态加密的私有机器学习分类

机器学习分类是通过分析大数据进行趋势预测的有用工具。由于支持对加密数据进行同态操作而无需解密，全同态加密（FHE）有助于机器学习分类，而不会泄露用户隐私，尤其是在外包场景中。在本文中，我们提出了一种基于 HElib 的改进 FHE 方案，这是一个基于 Brakerski 的 FHE 方案实现的 FHE 库。我们的改进主要集中在两个方面。一方面，我们首先使用重新线性化技术来减小密文大小，然后使用模数切换技术来降低模数和解密噪声。另一方面，如果乘法密文中存在加法同态或没有同态运算，则不需要重新线性化和模数切换' s 下一个同态操作。同态比较协议、基于私有超平面决策的分类和私有朴素贝叶斯分类首先通过加法同态和乘法同态实现。在我们的同态比较协议中，交互次数从 3 减少到 1。我们选择建议的 FHE 方案来实现私有决策树分类。仿真结果表明，我们的 FHE 方案的效率和私有决策树分类的实现比其他两种方案更有效。交互次数从 3 减少到 1。我们选择建议的 FHE 方案来实现私有决策树分类。仿真结果表明，我们的 FHE 方案的效率和私有决策树分类的实现比其他两种方案更有效。交互次数从 3 减少到 1。我们选择建议的 FHE 方案来实现私有决策树分类。仿真结果表明，我们的 FHE 方案的效率和私有决策树分类的实现比其他两种方案更有效。