XORBoost: Tree Boosting in the Multiparty Computation Setting

Authors: Kevin Deforth (Inpher, Switzerland), Marc Desgroseilliers (Inpher, Switzerland), Nicolas Gama (Inpher, Switzerland), Mariya Georgieva (Inpher, Switzerland), Dimitar Jetchev (Inpher, Switzerland), Marius Vuille (Inpher, Switzerland)

Volume: 2022
Issue: 4
Pages: 66–85
DOI: https://doi.org/10.56553/popets-2022-0099

Download PDF

Abstract: We present a novel protocol XORBoost for both training gradient boosted tree models and for using these models for inference in the multiparty computation (MPC) setting. Our protocol supports training for generically split datasets (vertical and horizontal splitting, or combination of those) while keeping all the information about features, thresholds, and evaluation paths private; only tree depth and the number of the binary trees are public parameters of the model. By using novel optimization techniques that reduce the number of oblivious permutation evaluations as well as sorting operations, we further speedup the algorithm. The protocol is agnostic to the underlying MPC framework or implementation.

Keywords: Privacy-preserving machine learning, multiparty computation, xgboosting

Copyright in PoPETs articles are held by their authors. This article is published under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 license.