24 citations · 41 across the 6 of their papers we have counts for
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cs.CR2022★ 24 cited
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
Lushan Song, Jiaxuan Wang, Zhexuan Wang +5
In order to perform machine learning among multiple parties while protecting the privacy of raw data, privacy-preserving machine learning based on secure multi-party computation (M…
cs.CR2022★ 2 cited
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy
Wenqiang Ruan, Mingxin Xu, Wenjing Fang +3
Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While…