SilentWood: Private Inference Over Gradient-Boosting Decision Forests

Abstract

Gradient boosting decision forests, used by XGBoost or AdaBoost, offer higher accuracy and lower training times than decision trees for large datasets. Protocols for private inference over decision trees can be used to preserve the privacy of the input data as well as the privacy of the trees. However, naively extending private inference over decision trees to private inference over decision forests by replicating the protocols leads to impractical running times. In this paper, we propose an efficient private decision inference protocol using homomorphic encryption. We present several optimizations that identify and then remove (approximate) duplication between the trees in a forest, thereby achieving significant improvements in communication and computation cost over the naive approach. To the best of our knowledge, we present the first private inference protocol for highly scalable gradient boosting decision forests. Our protocol's (SilentWood) inference time is faster than the baseline of parallel running the RCC-PDTE protocol by Mahdavi et al. by up to 42.5x, and faster than Zama's Concrete ML XGBoost by up to 27.8x, and faster than SoK-GGG's two-party garbled circuit protocol by 2.94x.