# Secure Collaborative Training and Inference for XGBoost

arXiv: https://arxiv.org/abs/2010.02524 · HTML: https://mlsanity.com/paper/2010.02524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Federated Boosted Decision Trees with Differential Privacy](https://mlsanity.com/paper/2210.02910/) (2022)
- [TEE-based decentralized recommender systems: The raw data sharing redemption](https://mlsanity.com/paper/2202.11655/) (2022)
- [SoK: Privacy-Preserving Collaborative Tree-based Model Learning](https://mlsanity.com/paper/2103.08987/) (2021)
- [Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning](https://mlsanity.com/paper/2303.05036/) (2023)
- [Proof-of-Guardrail in AI Agents and What (Not) to Trust from It](https://mlsanity.com/paper/2603.05786/) (2026)
- [ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies](https://mlsanity.com/paper/2110.11446/) (2021)
- [TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems](https://mlsanity.com/paper/2506.07605/) (2025)
- [TrEEStealer: Stealing Decision Trees via Enclave Side Channels](https://mlsanity.com/paper/2604.18716/) (2026)

## Abstract (arXiv)

In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations have also encountered obstacles in sharing sensitive data due to business, regulatory, and liability concerns. We propose Secure XGBoost, a privacy-preserving system that enables multiparty training and inference of XGBoost models. Secure XGBoost protects the privacy of each party's data as well as the integrity of the computation with the help of hardware enclaves. Crucially, Secure XGBoost augments the security of the enclaves using novel data-oblivious algorithms that prevent access side-channel attacks on enclaves induced via access pattern leakage.