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SoK: Training Machine Learning Models over Multiple Sources with Privacy
  Preservation
v1v2 (latest)

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

6 December 2020
Lushan Song
Guopeng Lin
Jiaxuan Wang
Haoqi Wu
Wenqiang Ruan
Weili Han
ArXiv (abs)PDFHTML

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

4 / 4 papers shown
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning
Wenqiang Ruan
Xin Lin
Ruisheng Zhou
Guopeng Lin
Shui Yu
Weili Han
290
1
0
16 Feb 2025
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
pMPL: A Robust Multi-Party Learning Framework with a Privileged PartyConference on Computer and Communications Security (CCS), 2022
Lushan Song
Jiaxuan Wang
Zhexuan Wang
Xinyu Tu
Guopeng Lin
Wenqiang Ruan
Haoqi Wu
Wei Han
379
27
0
02 Oct 2022
Private, Efficient, and Accurate: Protecting Models Trained by
  Multi-party Learning with Differential Privacy
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential PrivacyIEEE Symposium on Security and Privacy (IEEE S&P), 2022
Wenqiang Ruan
Ming Xu
Wenjing Fang
Li Wang
Lei Wang
Wei Han
264
22
0
18 Aug 2022
Report: State of the Art Solutions for Privacy Preserving Machine
  Learning in the Medical Context
Report: State of the Art Solutions for Privacy Preserving Machine Learning in the Medical Context
J. Zalonis
Frederik Armknecht
Björn Grohmann
Manuel Koch
214
4
0
27 Jan 2022
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