  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.
