# Privacy-Preserving Tree-Based Inference with TFHE

arXiv: https://arxiv.org/abs/2303.01254 · HTML: https://mlsanity.com/paper/2303.01254/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Privacy Preserving Machine Learning: Threats and Solutions](https://mlsanity.com/paper/1804.11238/) (2018)
- [Privacy-Preserving XGBoost Inference](https://mlsanity.com/paper/2011.04789/) (2020)
- [Non-Interactive Private Decision Tree Evaluation](https://mlsanity.com/paper/1909.08362/) (2019)
- [Cryptotree: fast and accurate predictions on encrypted structured data](https://mlsanity.com/paper/2006.08299/) (2020)

## Built on by (most cited first, 1 of 1)

- [Inhibitor Transformers and Gated RNNs for Torus Efficient Fully Homomorphic Encryption](https://mlsanity.com/paper/2308.05629/) (2023)

## Abstract (arXiv)

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.