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Secure Collaborative Training and Inference for XGBoost
[article]
2020
arXiv
pre-print
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
arXiv:2010.02524v1
fatcat:mh5gmhwqefe7lfh25jet2jgpzq