Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for arbitrary computations to be performed on encrypted data. FHE has received lots of attention in the past few years and has reached realistic execution times and correctness. More precisely, we explain in this paper how we apply FHE to tree-based models and get state-of-the-art solutions over encrypted tabular data. We show that our method is applicable to a wide range of tree-based models, including decision trees, random forests, and gradient boosted trees, and has been implemented within the Concrete-ML library, which is open-source at https://github.com/zama-ai/concrete-ml. With a selected set of use-cases, we demonstrate that our FHE version is very close to the unprotected version in terms of accuracy.

翻译：隐私增强技术（PETs）被提出作为一种在保障数据隐私的同时进行数据分析的方法。本文聚焦于全同态加密（FHE）技术，该强大工具允许对加密数据进行任意计算。近年来，FHE受到广泛关注，已达到实用的执行时间和正确性。具体而言，我们阐释了如何将FHE应用于树模型，从而在加密表格数据上实现最先进的解决方案。研究表明，该方法适用于包括决策树、随机森林和梯度提升树在内的多种树模型，并已集成至开源库Concrete-ML（代码仓库：https://github.com/zama-ai/concrete-ml）。通过选定用例，我们验证了FHE版本在精度上与非加密版本高度接近。