most citedPrivate, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy

2 citations · 2 across the 1 of their papers we have counts for

collaborators

1 paper

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…