Secure XGBoost Documentation

Secure XGBoost is a secure gradient boosted decision tree library based off the popular XGBoost project that supports scalable, distributed, and efficient gradient boosting. In addition to offering the efficiency, flexibility, and portability that vanilla XGBoost does to solve a variety of problems, Secure XGBoost enables secure collaborative learning by leveraging hardware enclaves and oblivious algorithms. This project allows multiple parties to each share their sensitive data to perform joint computation without revealing the contents of the data.

This project is currently under development as part of the broader Multiparty Collaboration and Coopetition effort by the UC Berkeley RISE Lab.

Secure XGBoost is open source, and we welcome contributions to our work here. For questions, please open an issue.

Contents

• About Secure XGBoost
• Installation Guide
• Get Started with Secure XGBoost
• Tutorials
  • Secure XGBoost in Jupyter
  • Outsourced Computation
  • Distributed Secure XGBoost
  • Multiclient Outsourced Distributed Computation
• Secure XGBoost Parameters
• Python package
  • Python Package Introduction
  • Python API Reference