This repo is a prototype implementation of privacy-preserving XGBoost (https://xgboost.readthedocs.io/en/latest/). We adopt several property-preserving encryption schemes to encrypt the XGBoost model so that the privacy-preserving model can predict an encrypted query.
An extended abstract of this work (https://arxiv.org/abs/2011.04789) appears in Privacy-preserving Machine Learning Workshop at NeurIPS 2020.
pip install pytest
When run the test files, first in the repo directory.
- pip3 install -r requirements.txt
Go to the test directory ('cd test'), run the following:
- python -m pytest
The OPE scheme is from the open source (https://github.com/tonyo/pyope). It implements the OPE scheme by Boldyreva et. al. The source code is place in the 'third-party/ope/' directory. We also leverage the partially homomorphic encryption scheme (Paillier Cryptosystem: https://en.wikipedia.org/wiki/Paillier_cryptosystem), run pip install phe to install this.
See DEVELOPMENT.md
See CONTRIBUTING for more information.
This project is licensed under the Apache-2.0 License.