Gibbon: Faster Secure Two-party Training of Gradient Boosting Decision Tree

Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry. They have achieved remarkable success in numerous machine learning competitions and practical applications. Secure Multi-Party Computation (MPC) allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Gibbon, a secure two-party GBDT training framework on a vertically split dataset, where two data owners each hold different features of the same data samples. Compared with the state-of-the-art Squirrel (USENIX'Sec 2023), for most parameter settings, Gibbon achieves 2×-4× reduction in running time and 2×-3× reduction in communication.