NodeGuard: A Highly Efficient Two-Party Computation Framework for Training Large-Scale Gradient Boosting Decision Tree

ePrint Report: NodeGuard: A Highly Efficient Two-Party Computation Framework for Training Large-Scale Gradient Boosting Decision Tree

Tianxiang Dai, Yufan Jiang, Yong Li, Fei Mei

The Gradient Boosting Decision Tree (GBDT) is a well-known machine learning algorithm, which achieves high performance and outstanding interpretability in real-world scenes such as fraud detection, online marketing and risk management. Meanwhile, two data owners can jointly train a GBDT model without disclosing their private dataset by executing secure Multi-Party Computation (MPC) protocols. In this work, …

algorithm computation dai decision eprint report fei framework high large machine machine learning party performance real report scale training well-known world