Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform for doing machine learning. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-aided machine learning. In this work, we investigate the study of how to efficiently do decision tree training and evaluation in the cloud and meanwhile achieve privacy preservation. Unlike existing cloud server-assisted model training approaches, in our proposed solution, the whole training process is mostly done by the cloud service provider who owns the machine learning model. Since the cloud cannot directly divide the encrypted dataset according to the best attributes selected, we propose a new method for decision tree training without dataset splitting. Precisely, we design three methods for decision tree training with the different tradeoff between privacy and efficiency. In all of these methods, the outsourced data are not revealed to the cloud service provider. We also propose a privacy-preserving decision tree evaluation scheme where the cloud service provider learns nothing about the user's input and the classification result while the trained model is kept secret to the user who could only learn the classification result. Compared with previous decision tree evaluation work, our scheme achieves desirable privacy preservation against both the user and the cloud service provider, and also minimizes the user's computation and communication costs. Moreover, besides protecting the data confidentiality, our proposed scheme also supports off-line users and thus has good scalability. The real-world dataset-based experimental results demonstrate that our system is of desirable utility and efficiency.

中文翻译：

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迈向云中实用的隐私保护决策树训练和评估


由于能够存储海量数据并提供巨大的计算资源，云计算已成为进行机器学习的理想平台。然而，数据隐私问题远未得到很好的解决，因此一直是云辅助机器学习中普遍关注的问题。在这项工作中，我们研究了如何在云端有效地进行决策树训练和评估，同时实现隐私保护。与现有的云服务器辅助模型训练方法不同，在我们提出的解决方案中，整个训练过程主要由拥有机器学习模型的云服务提供商完成。由于云无法根据选择的最佳属性直接划分加密数据集，因此我们提出了一种无需数据集拆分的决策树训练新方法。准确地说，我们设计了三种决策树训练方法，在隐私和效率之间进行了不同的权衡。在所有这些方法中，外包数据都不会泄露给云服务提供商。我们还提出了一种隐私保护决策树评估方案，其中云服务提供商对用户的输入和分类结果一无所知，而训练后的模型对只能了解分类结果的用户保密。与之前的决策树评估工作相比，我们的方案对用户和云服务提供商都实现了理想的隐私保护，并且还最小化了用户的计算和通信成本。此外，除了保护数据机密性外，我们提出的方案还支持离线用户，因此具有良好的可扩展性。基于现实世界数据集的实验结果表明，我们的系统具有理想的实用性和效率。