Secure Collaborative Training and Inference for XGBoost

6 October 2020

Papers citing "Secure Collaborative Training and Inference for XGBoost"

8 / 8 papers shown

Title
Metadata-private Messaging without Coordination Peipei Jiang Yihao Wu Lei Xu Wentao Dong Peiyuan Chen Yulong Ming Cong Wang Xiaohua Jia Qian Wang 25 0 0 28 Apr 2025
GTree: GPU-Friendly Privacy-preserving Decision Tree Training and Inference Qifan Wang Shujie Cui Lei Zhou Ye Dong Jianli Bai Yun Sing Koh Giovanni Russello 33 0 0 01 May 2023
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning AprilPyone Maungmaung Hitoshi Kiya FedML DiffM 24 3 0 09 Mar 2023
Federated Boosted Decision Trees with Differential Privacy Samuel Maddock Graham Cormode Tianhao Wang Carsten Maple S. Jha FedML 42 29 0 06 Oct 2022
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context J. Zalonis Frederik Armknecht Björn Grohmann Manuel Koch 33 4 0 27 Jan 2022
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
SoK: Privacy-Preserving Collaborative Tree-based Model Learning Sylvain Chatel Apostolos Pyrgelis J. Troncoso-Pastoriza Jean-Pierre Hubaux 17 14 0 16 Mar 2021
FederBoost: Private Federated Learning for GBDT Zhihua Tian Rui Zhang Xiaoyang Hou Jian-wei Liu K. Ren Jian Liu Kui Ren FedML AI4CE 47 66 0 05 Nov 2020