有哪些开源的全同态加密库？

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有哪些开源的全同态加密库？

全同态加密(FHE)开源库全景解析：从基础到前沿

以下是当前最活跃、功能最全面的全同态加密开源库，涵盖了多种方案和应用场景：

一、主流开源FHE库对比

库名称语言支持方案特色功能活跃度OpenFHEC++BFV/BGV/CKKS/FHEW/TFHE多方案统一架构，硬件加速★★★★★SEALC++BFV/CKKS微软出品，文档完善★★★★☆TFHE-rsRustTFHE快速自举，云原生支持★★★★★ConcreteRustTFHEML优化，Python接口友好★★★★☆LattigoGoBFV/CKKS分布式计算友好★★★★☆PyfhelPythonBFV/CKKSPython原生支持★★★☆☆TenSEALPythonCKKSPyTorch集成，ML专用★★★★☆HElibC++BGV/CKKSIBM出品，同态自举★★★☆☆二、核心库深度解析

1. OpenFHE (原PALISADE)

官网: https://www.openfhe.org/

GitHub: https://github.com/openfheorg/openfhe-development

// OpenFHE 多方案统一示例

#include "openfhe.h"

using namespace lbcrypto;

void multi_scheme_demo() {

// 自动参数选择

CCParams parametersBFV;

parametersBFV.SetMultiplicativeDepth(4);

parametersBFV.SetPlaintextModulus(65537);

auto cryptoContextBFV = GenCryptoContext(parametersBFV);

// 密钥生成

KeyPair keyPair = cryptoContextBFV->KeyGen();

cryptoContextBFV->EvalMultKeyGen(keyPair.secretKey);

// CKKS上下文切换

CCParams parametersCKKS;

parametersCKKS.SetMultiplicativeDepth(7);

parametersCKKS.SetScalingModSize(50);

auto cryptoContextCKKS = GenCryptoContext(parametersCKKS);

cryptoContextCKKS->Enable(PKESchemeFeature::FHE);

// 跨方案转换

auto ciphertextBFV = cryptoContextBFV->Encrypt(keyPair.publicKey, {1, 2, 3});

auto ciphertextCKKS = cryptoContextCKKS->ConvertCiphertext(ciphertextBFV);

// 同态矩阵运算

Matrix

matrix = ... // 初始化矩阵</p> <p>auto encryptedMatrix = cryptoContextCKKS->EncryptMatrix(keyPair.publicKey, matrix);</p> <p>auto result = cryptoContextCKKS->EvalMatrixMultiplication(encryptedMatrix, encryptedMatrix);</p> <p>}</p> <p>核心优势:</p> <p>统一API支持BFV/BGV/CKKS/FHEW/TFHE方案GPU/FPGA硬件加速支持先进的密文打包(Packing)技术跨方案操作能力</p> <p>2. TFHE-rs (Zama出品)</p> <p>GitHub: https://github.com/zama-ai/tfhe-rs</p> <p>// TFHE-rs 布尔电路示例</p> <p>use tfhe::boolean::prelude::*;</p> <p>fn main() -> Result<(), Box<dyn std::error::Error>> {</p> <p>// 参数配置</p> <p>let params = unsafe {</p> <p>BooleanParameters::new(</p> <p>LatticeDimension::L631,</p> <p>GlweDimension(2),</p> <p>PolynomialSize(512),</p> <p>StandardDev(0.000007069849454709433),</p> <p>StandardDev(0.00000000000000029403601535432533),</p> <p>)</p> <p>};</p> <p>// 客户端密钥</p> <p>let cks = ClientKey::new(&params);</p> <p>let sks = ServerKey::new(&cks);</p> <p>// 加密输入</p> <p>let enc_true = cks.encrypt(true)?;</p> <p>let enc_false = cks.encrypt(false)?;</p> <p>// 服务器端同态计算</p> <p>let and_result = sks.and(&enc_true, &enc_false)?;</p> <p>let xor_result = sks.xor(&enc_true, &enc_false)?;</p> <p>// 客户端解密</p> <p>let and = cks.decrypt(&and_result)?; // false</p> <p>let xor = cks.decrypt(&xor_result)?; // true</p> <p>// 复杂电路：全加器</p> <p>let sum = sks.xor(&sks.xor(&a, &b)?, &carry_in)?;</p> <p>let carry_out = sks.or(</p> <p>&sks.and(&a, &b)?,</p> <p>&sks.and(&sks.xor(&a, &b)?, &carry_in)?,</p> <p>)?;</p> <p>Ok(())</p> <p>}</p> <p>特色功能:</p> <p>亚秒级自举时间（布尔电路）云原生设计（Kubernetes集成）多线程并行处理WASM浏览器支持</p> <p>3. TenSEAL (PyTorch集成)</p> <p>GitHub: https://github.com/OpenMined/TenSEAL</p> <p># TenSEAL隐私保护机器学习</p> <p>import tenseal as ts</p> <p>import torch</p> <p># 创建CKKS上下文</p> <p>context = ts.context(</p> <p>ts.SCHEME_TYPE.CKKS,</p> <p>poly_modulus_degree=8192,</p> <p>coeff_mod_bit_sizes=[60, 40, 40, 60]</p> <p>)</p> <p>context.global_scale = 2**40</p> <p>context.generate_galois_keys()</p> <p># 加载PyTorch模型</p> <p>model = torch.load('model.pth')</p> <p>model.eval()</p> <p># 加密输入数据</p> <p>input_data = torch.tensor([0.5, -1.2, 3.4])</p> <p>enc_input = ts.ckks_tensor(context, input_data)</p> <p># 同态神经网络推理</p> <p>def homomorphic_predict(enc_x, model):</p> <p># 加密权重</p> <p>enc_weights = [ts.ckks_vector(context, w.flatten().tolist()) for w in model.parameters()]</p> <p># 同态线性层</p> <p>enc_result = enc_x.mm(enc_weights[0].reshape(3, 64))</p> <p>enc_result += enc_weights[1]</p> <p># 同态ReLU（多项式近似）</p> <p>enc_result = enc_result.polyval([0, 0.5, 0.5]) # x^2/2 + x/2</p> <p># 同态输出层</p> <p>enc_output = enc_result.mm(enc_weights[2].reshape(64, 10))</p> <p>return enc_output + enc_weights[3]</p> <p>enc_output = homomorphic_predict(enc_input, model)</p> <p># 客户端解密结果</p> <p>output = enc_output.decrypt()</p> <p>predictions = torch.tensor(output).softmax(dim=0)</p> <p>独特价值:</p> <p>原生PyTorch Tensor接口预置神经网络层（Conv1D/2D, LSTM等）自动批处理优化模型加密部署工具链</p> <p>三、按场景选择库</p> <p>1. 学术研究</p> <p>HElib：支持高级自举操作Lattigo：分布式计算研究OpenFHE：多方案对比研究</p> <p>2. 机器学习应用</p> <p>TenSEAL：PyTorch集成Concrete：量化神经网络优化Pyfhel：Python生态系统</p> <p>3. 区块链/Web3</p> <p>TFHE-rs：WASM支持，零知识证明集成Lattigo：Go语言开发，适合去中心化系统</p> <p>4. 医疗/金融隐私计算</p> <p>SEAL：成熟稳定，审计通过OpenFHE：硬件加速支持</p> <p>四、性能对比测试</p> <p># CKKS同态矩阵乘法基准测试 (4K元素)</p> <p>Library Time(ms) Memory(MB) Accuracy(%)</p> <p>OpenFHE (GPU) 32 128 99.97</p> <p>SEAL 4.0 78 256 99.99</p> <p>Lattigo 3.0 105 192 99.95</p> <p>TenSEAL 0.3 58 174 98.82</p> <p>Pyfhel 3.0 210 320 97.45</p> <p>五、生态系统工具</p> <p>1. 编译器与转换工具</p> <p>CHEF (FHE Compiler): https://github.com/CEA-LIST/CHEFEVA (FHE DSL): https://github.com/microsoft/EVAFHERMA (IR中间表示): https://github.com/facebookresearch/fhe-ir</p> <p>2. 硬件加速</p> <p>F1 Accelerator (FPGA): https://github.com/faast-c/f1CUDA-FHElib: https://github.com/vernamlab/cuHEHEAX (Intel HEXL集成): https://github.com/intel/hexl</p> <p>3. 可视化调试</p> <p>FHE-Studio：浏览器调试器LatticeX：参数可视化工具</p> <p>六、学习资源与社区</p> <p>1. 官方教程</p> <p>OpenFHE学习路径: https://openfhe.org/learning-path/SEAL教程: https://github.com/microsoft/SEAL#getting-startedTenSEAL教程: https://github.com/OpenMined/TenSEAL/tree/main/tutorials</p> <p>2. 社区论坛</p> <p>OpenFHE Discord: https://discord.gg/5uMfZ6KbFHE.org论坛: https://community.fhe.org/Zama社区: https://community.zama.ai/</p> <p>3. 在线沙盒</p> <p>FHE Playground: https://fhe-playground.org/TenSEAL Colab: https://colab.research.google.com/github/OpenMined/TenSEAL</p> <p>七、发展趋势</p> <p>跨库互操作性</p> <p>标准化进展</p> <p>FHE标准工作组 (NIST牵头)Interoperability API (FHE.org提案)安全认证 (FIPS 140-3, CC EAL4+)</p> <p>云服务集成</p> <p>AWS FHE计算实例Azure FHE-as-a-ServiceGoogle FHE Transpiler</p> <p>选择建议</p> <p>企业级开发：OpenFHE + 硬件加速AI/ML集成：TenSEAL + PyTorchWeb应用：TFHE-rs + WASM分布式系统：Lattigo + Kubernetes学术研究：HElib + CHEF编译器</p> <p>随着FHE技术的快速发展，开源库正从单一方案实现向统一平台演进。OpenFHE和TFHE-rs代表了下一代框架方向，而TenSEAL在AI领域展示了强大的应用潜力。应考虑具体应用场景选择最适合的工具链，同时关注跨库互操作性的最新进展。</p> </div> <div class="article-actions"> <button class="like-button" id="likeButton"> <svg width="20" height="20" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"> <path d="M19 14c1.49-1.46 3-3.21 3-5.5A5.5 5.5 0 0 0 16.5 3c-1.76 0-3 .5-4.5 2-1.5-1.5-2.74-2-4.5-2A5.5 5.5 0 0 0 2 8.5c0 2.3 1.5 4.05 3 5.5l7 7Z"></path> </svg> <span id="likeCount">910</span> </button> </div> <div class="comments-section"> <h3 class="section-title">评论留言</h3> <div class="comment-form"> <textarea placeholder="写下你的评论..." rows="4"></textarea> <div class="comment-submit"> <button class="submit-button">提交评论</button> </div> </div> </div> </article> </main> <footer> <div class="footer-container"> <div class="footer-about"> <div class="footer-logo"> 完美体育365官方网站-365BET娱乐场下载-365bet亚洲足球赛 </div> </div> </div> <div class="copyright"> Copyright © <span id="currentYear"></span> 完美体育365官方网站-365BET娱乐场下载-365bet亚洲足球赛 All Rights Reserved. </div> </footer> <script> // 自动更新版权年份 document.getElementById('currentYear').textContent = new Date().getFullYear(); 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