Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree

ePrint Report: Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree

Wen-jie Lu, Zhicong Huang, Qizhi Zhang, Yuchen Wang, Cheng Hong

Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry, due to their strong interpretability. Secure multi-party computation allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Squirrel, a two-party GBDT training framework on a vertically split dataset, where two data owners …

computation compute data decision eprint report features framework function industry input party report squirrel training work