Secure collaborative training and inference for xgboost
Proceedings of the 2020 workshop on privacy-preserving machine learning in …, 2020•dl.acm.org
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 …
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 …
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.
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