MPC，Secure Multi-party Computation

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原创最新推荐文章于 2026-05-02 10:53:24 发布 561 阅读 AI 写同款· GEO 优化 ›

Secure multi-party computation (MPC) 介绍文章来源：https://zhuanlan.zhihu.com/p/100648606 1 百万富翁问题两个百万富翁都想比较到底谁更富有，但是有都不想让别人知道自己有多少钱。在没有可信的第三方的情况下如何进行？这个问题就是著名的姚式百万富翁问题。姚式，即大名鼎鼎的姚期智，我国唯一一个图灵奖获得者，此问题开创了安全多方计算领域。为了说明，这里先简化问题，将两个富翁的财产限定在1千万到1亿之间，而且只想做千万级的比较。也就是说：两个富翁，分别为张三和李四。他们自己都清楚自己有几千万财产，也即，他们阅读详情

It allows a group of users to jointly compute a function with their private data as input, and all users can only get the output of this function, without getting any other information.

The goal of MPC is to allow distributed participants to cooperate in computing any function without revealing the privacy input and output of the participants. For example, suppose there are three people A, B, and C, they want to calculate their average salary, but they don’t want to tell others how much their salary is, then they can use secure multi-party computation, with their salaries as input, and calculate the average salary as output.

The implementation of MPC technology is usually based on a variety of cryptographic technologies1, including Secret Sharing, Oblivious Transfer Protocol, Garbled Circuit, and Homomorphic Encryption, etc12. These technologies can protect the input of each participant, and at the same time, the real calculation result can be restored when aggregating at the end.

“安全多方计算”（MPC，Secure Multi-party Computation）是一种隐私计算技术。它允许一组用户以他们的隐私数据为输入，共同计算一个函数，并且所有用户只能得到这个函数的输出，无法得到其他的任何信息

MPC技术的实现通常基于密码学的多种技术，包括秘密分享（Secret Sharing）、不经意传输协议（Oblivious Transfer）、混淆电路（Garbled Circuit）和同态加密（Homomorphic Encryption）等。这些技术可以保护每个参与方的输入，同时在最后聚合时，可以还原出真实的计算结果

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