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pMPL: A Robust Multi-Party Learning Framework with a Privileged Party. (arXiv:2210.00486v1 [cs.CR])
Oct. 4, 2022, 1:20 a.m. | Lushan Song, Jiaxuan Wang, Zhexuan Wang, Xinyu Tu, Guopeng Lin, Wenqiang Ruan, Haoqi Wu, Weili Han
cs.CR updates on arXiv.org arxiv.org
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 (MPL for short) has been a hot spot in recent. The
configuration of MPL usually follows the peer-to-peer architecture, where each
party has the same chance to reveal the output result. However, typical
business scenarios often follow a hierarchical architecture where a powerful,
usually \textit{privileged party}, leads the tasks of machine learning. Only
the \textit{privileged party} …
More from arxiv.org / cs.CR updates on arXiv.org
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