EzBoost: Fast And Secure Vertical Federated Tree Boosting Framework via EzPC

Federated learning (FL) has emerged as a prominent methodology for collaboratively training machine learning models among multiple participants while alleviating data privacy leakage through data localization. However, recent studies have shown that the transferred intermediate parameters still contain sensitive information that needs to be further protected. Moreover, real-world institutions often possess diverse data attributes, necessitating the adoption of Vertical Federated Learning (VFL) for cooperative learning tasks. Existing researches in VFL have proposed some frameworks with privacy-preservation functionality, yet they suffer from high participant overhead or low model accuracy, etc. To address these challenges, in this paper, we propose EzBoost, a fast and secure vertical federated tree boosting framework built upon XGBoost. Specifically, we leverages the efficient Secure Multi-party Computation (MPC) framework, EzPC, to facilitate the design and implementation of EzBoost. By carefully designing our framework with two non-collusive servers for secure two-party computation, EzBoost significantly accelerates the runtime of model training and querying at most 20x, and reduces the participant overheads at most 300x. In addition, we identify a potential privacy leakage problem in recent researches and propose a more robust solution for addressing it. Through comprehensive security analysis and comparative experiments with existing approaches, we demonstrate that EzBoost achieves stronger privacy-preservation, higher accuracy and higher efficiency simultaneously.