pMPL: A Robust Multi-Party Learning Framework with a Privileged Party. (arXiv:2210.00486v4 [cs.CR] UPDATED)

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 privileged party, leads the tasks of machine learning. Only the
privileged party …

framework party privileged