# Privacy-Preserving XGBoost Inference

arXiv: https://arxiv.org/abs/2011.04789 · HTML: https://mlsanity.com/paper/2011.04789/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies](https://mlsanity.com/paper/2110.11446/) (2021)
- [Privacy-Preserving Tree-Based Inference with TFHE](https://mlsanity.com/paper/2303.01254/) (2023)
- [Towards Industrial Private AI: A two-tier framework for data and model security](https://mlsanity.com/paper/2107.12806/) (2021)
- [Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption](https://mlsanity.com/paper/2211.06675/) (2022)

## Abstract (arXiv)

Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential. A major barrier to adoption is the sensitive nature of predictive queries. Individual users may lack sufficiently rich datasets to train accurate models locally but also be unwilling to send sensitive queries to commercial services that vend such models. One central goal of privacy-preserving machine learning (PPML) is to enable users to submit encrypted queries to a remote ML service, receive encrypted results, and decrypt them locally. We aim at developing practical solutions for real-world privacy-preserving ML inference problems. In this paper, we propose a privacy-preserving XGBoost prediction algorithm, which we have implemented and evaluated empirically on AWS SageMaker. Experimental results indicate that our algorithm is efficient enough to be used in real ML production environments.