pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
Lushan Song, Jiaxuan Wang, Zhexuan Wang, Xinyu Tu, G. Lin, Wenqiang Ruan, Haoqi Wu, Weili Han
Abstract
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, usuallyprivileged party, leads the tasks of machine learning. Only theprivileged party can reveal the final model even if otherassistant parties collude with each other. It is even required to avoid the abort of machine learning to ensure the scheduled deadlines and/or save used computing resources when part ofassistant parties drop out.