论文标题
加密图：对加密结构化数据的快速准确预测
Cryptotree: fast and accurate predictions on encrypted structured data
论文作者
论文摘要
将机器学习算法应用于私人数据，例如财务或医疗数据，同时保留其机密性，这是一项艰巨的任务。同态加密（HE）因其允许对输入和输出加密的加密数据计算的能力而得到认可，因此可以对私人数据进行安全推断。尽管如此，由于HE的限制，例如无法评估非多功能功能或有效执行任意矩阵乘法，因此仅在迄今为止的HE范式中，线性模型的推断似乎可以使用。 在本文中，我们提出了加密图，即在HE的背景下，可以使用随机森林（RF）的框架（RF），这是一种非常有力的学习程序。为此，我们首先将常规RF转换为神经RF，然后对其进行调整以适合HE计划CKK，从而使他可以在实际值上运行。通过SIMD操作，我们能够比加密数据上的原始RF获得快速的推断和预测结果。
Applying machine learning algorithms to private data, such as financial or medical data, while preserving their confidentiality, is a difficult task. Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted, which therefore enables secure inference on private data. Nonetheless, because of the constraints of HE, such as its inability to evaluate non-polynomial functions or to perform arbitrary matrix multiplication efficiently, only inference of linear models seem usable in practice in the HE paradigm so far. In this paper, we propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression, in the context of HE. To this aim, we first convert a regular RF to a Neural RF, then adapt this to fit the HE scheme CKKS, which allows HE operations on real values. Through SIMD operations, we are able to have quick inference and prediction results better than the original RF on encrypted data.
