pMPL：一个具有多个参与方的健壮多方学习框架
pMPL: A Robust Multi-Party Learning Framework with a Privileged Party
摘要：
为了在多方之间进行机器学习，同时保护原始数据的隐私，基于安全多方计算（Secure Multi-Party Computation，简称MPL）的隐私保护机器学习成为近年来的研究热点。MPL的配置通常遵循对等体系结构，其中每一方都有相同的机会显示输出结果。然而，典型的业务场景通常遵循分层架构，其中一个强大的，通常是特权的一方，领导机器学习的任务。即使其他辅助方相互勾结，也只有特权方才能揭示最终模型。甚至需要避免机器学习的中止，以确保预定的最后期限和/或在部分助理方退出时节省所使用的计算资源。受上述场景的启发，我们提出了pMPL，一个具有特权部分的健壮MPL框架。pMPL支持在半诚实环境中的三方培训。通过为特权方设置备用份额，pMPL是健壮的，可以容忍其余两方中的一方在训练期间退出。在上述设置的基础上，我们为pMPL设计了一系列基于向量空间秘密共享的高效协议，以弥合向量空间秘密共享和机器学习之间的差距。最后，实验结果表明，pMPL的性能是有前途的，当我们比较它与国家的最先进的MPL框架。特别是，在LAN设置中，对于线性回归和逻辑回归，pMPL分别比TF加密的（以ABY 3作为后端框架）快大约$16\times$和$5\times$。此外，线性回归、逻辑回归和BP神经网络的训练模型在MNIST数据集上的准确率分别达到97%、99%和96%左右。
英文摘要：
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 can reveal the final model even if other assistant 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 of assistant parties drop out. Motivated by the above scenarios, we propose pMPL, a robust MPL framework with a privileged part}. pMPL supports three-party training in the semi-honest setting. By setting alternate shares for the privileged party, pMPL is robust to tolerate one of the rest two parties dropping out during the training. With the above settings, we design a series of efficient protocols based on vector space secret sharing for pMPL to bridge the gap between vector space secret sharing and machine learning. Finally, the experimental results show that the performance of pMPL is promising when we compare it with the state-of-the-art MPL frameworks. Especially, in the LAN setting, pMPL is around $16\times$ and $5\times$ faster than TF-encrypted (with ABY3 as the back-end framework) for the linear regression, and logistic regression, respectively. Besides, the accuracy of trained models of linear regression, logistic regression, and BP neural networks can reach around 97%, 99%, and 96% on MNIST dataset respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
