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：隐私增强技术被提出用于在允许数据分析的同时保护数据隐私。本文聚焦于全同态加密（FHE）这一可在加密数据上执行任意计算的强大工具。近年来FHE备受关注，已实现实际可行的执行时间与正确性。具体而言，本文阐释了如何将FHE应用于基于树的模型，从而在加密表格数据上获得最先进的解决方案。我们证明该方法适用于包括决策树、随机森林及梯度提升树在内的广泛树模型，并已通过Concrete-ML开源库（https://github.com/zama-ai/concrete-ml）实现。通过选定用例，我们展示了FHE版本在精度上与非保护版本高度接近。