SoK: Training Machine Learning Models over Multiple Sources with Privacy
Preservation

SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

6 December 2020

Haoqi Wu

Papers citing "SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation"

5 / 5 papers shown

Title
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party Lushan Song Jiaxuan Wang Zhexuan Wang Xinyu Tu Guopeng Lin Wenqiang Ruan Haoqi Wu Wei Han 13 18 0 02 Oct 2022
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy Wenqiang Ruan Ming Xu Wenjing Fang Li Wang Lei Wang Wei Han 32 12 0 18 Aug 2022
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context J. Zalonis Frederik Armknecht Björn Grohmann Manuel Koch 17 4 0 27 Jan 2022
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU Sijun Tan Brian Knott Yuan Tian David J. Wu BDL FedML 57 183 0 22 Apr 2021
CrypTFlow2: Practical 2-Party Secure Inference Deevashwer Rathee Mayank Rathee Nishant Kumar Nishanth Chandran Divya Gupta Aseem Rastogi Rahul Sharma 79 301 0 13 Oct 2020