---
title: "什么是 全同态加密（FHE）？"
canonical: https://wavect.io/zh/glossary/homomorphic-encryption/
language: zh
description: "一种密码学技术，让服务器可以在从不解密的前提下对加密数据进行计算。"
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

技术

# 同态加密

全同态加密（FHE）

一种密码学技术，让服务器可以在从不解密的前提下对加密数据进行计算。

最近审阅: 2026-07-05 审阅人 [Kevin Riedl](/zh/team/kevin-riedl/) [wiki ↗](https://www.wikidata.org/wiki/Q139796365)

同态加密让别人可以在你的数据上运行计算，而数据全程保持加密。客户端加密输入，服务器蒙着眼计算，只有客户端能解密结果。服务器什么都学不到，连答案都学不到。全同态加密（FHE）是它的一般形态：任意计算、不限深度，但要付出代价。

代价就是整个故事。FHE 比同一计算的明文版本慢约 1,000 倍到 10,000 倍，这排除了交互式负载和任何前沿模型尺寸的东西。真正在生产环境交付的是那个窄模式：私有查询和小而明确的计算。Apple 的 Live Caller ID Lookup 在不透露电话号码的前提下把陌生来电与服务器数据库比对（BFV 方案），Microsoft Edge 在看不到密码的前提下把它与泄露库比对，Zama 的协议在以太坊上以每秒几十笔交易结算加密交易。没有人把后端跑在 FHE 下，包括口袋最深的那几家公司。

在实践中，「FHE」是一个方案家族，各有各的甜区：TFHE 适合加密逻辑和比较，CKKS 适合近似数和机器学习，BGV/BFV 适合精确整数查询。工具链已经开源，强工程团队用得起来，但库的选择取决于工作负载：TFHE-rs 是成熟的 TFHE 起点，Apple 的 Swift 库适合 BFV 查询，而 CKKS 值得在 Poulpy、Lattigo、SEAL、OpenFHE 和 FIDESlib 这类 GPU 原生选项间做基准测试。参数选择和性能工程仍是专家活。如果目标是证明一个事实而不是在隐藏数据上计算， [零知识证明](/zh/glossary/zero-knowledge/) 是更好的工具；如果硬件信任根可以接受，可信执行环境能以接近原生的速度干同样的活。我们的 [FHE 深潜](/zh/blog/fully-homomorphic-encryption-practical-2026/) 载有有出处的 2026 年数字， [决策框架](/zh/blog/zk-vs-fhe-vs-mpc-vs-tee/) 则讲了 FHE 什么时候胜过替代方案。

// FAQ

## 常见问题

### 2026 年 FHE 实用吗？

对窄负载，实用：私有查询（Apple 和 Microsoft 的模式）、小模型 ML 推理，以及每秒几十笔交易的加密逻辑。对通用或实时计算，不实用：相对明文的开销仍是三到四个数量级。

### FHE 和零知识证明的区别是什么？

ZK 在不出示证据的前提下证明一个陈述为真；FHE 在始终保持加密的数据上计算。第三方必须验证某件事时用 ZK，一个不受信的一方必须在它永远不许看到的数据上计算时用 FHE。两者组合得很好：很多架构同时用到两个。

### 我们该从哪个 FHE 库开始？

加密逻辑用 Zama 的 TFHE-rs，BFV 私有查询用 Apple 的 swift-homomorphic-encryption。CKKS 分析应在真实负载上测试 Poulpy、Lattigo、SEAL、OpenFHE 和 FIDESlib 这类 GPU 原生选项；OpenFHE 功能面广，但不自动等于性能领先。

相关术语

- [Zero-Knowledge](/zh/glossary/zero-knowledge/)
- [LLM](/zh/glossary/llm/)

相关服务

- [AI 与前沿技术](/zh/services/bleeding-edge/)

相关案例研究

- [Twinsoft AI](/zh/case-studies/twinsoft-ai/)

相关指南

- [如何为 MVP 选择技术栈](/zh/software-development-guide/how-to-choose-a-tech-stack-for-mvp/)

聊聊

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