The classification is one of the important machine learning technologies, which plays an important role in the fields of medical treatment, image processing, and cyberspace security. In recent years, the popularity of cloud computing has caused many data owners to seek outsourced storage computing, which has become a new trend in the development of classification. Although it can be used in several research areas, such as medical and financial, this paradigm introduces new security and privacy problems. In response to the ciphertext classification requirements in different scenarios in outsourcing environments, this paper proposed an outsourced privacy-preserving decision tree classification model based on homomorphic encryption, which can hide sensitive inputs (model and query) and outputs (analytic result) from a counterparty. A secure exclusive OR (XOR) protocol is created; it intelligently computes the XOR of two bit-encrypted data to obtain another bit-encrypted result. The experimental results show that the proposed protocols are particularly efficient for outsourced decision trees, which are encrypted and stored in the cloud. These decision trees are typical of classification models trained from real datasets, ranging from heart disease to cancer classification.

分类是重要的机器学习技术之一，它在医疗，图像处理和网络空间安全领域中发挥着重要作用。近年来，云计算的普及导致许多数据所有者寻求外包存储计算，这已成为分类发展的新趋势。尽管可以在医疗和金融等多个研究领域中使用它，但这种范例引入了新的安全性和隐私问题。针对外包环境中不同场景下密文分类的需求，提出了一种基于同态加密的外包隐私保护决策树分类模型，该模型可以隐藏交易对手的敏感输入（模型和查询）和输出（分析结果）。 。创建安全的异或（XOR）协议；它可以智能地计算两个位加密数据的XOR，以获得另一个位加密结果。实验结果表明，所提出的协议对于外包的决策树特别有效，该决策树经过加密并存储在云中。这些决策树是从真实数据集训练的分类模型的典型代表，从心脏病到癌症分类。