├── README.md ├── 安全多方计算 ├── A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority.pdf ├── ABY&ABY2.0.pdf ├── Fast Large-scale honest-majority MPC for malicious adversaries.pdf ├── Look-Up Table.pdf ├── MOTION-A Framework for Mixed-Protocol Multi-Party Computation.pdf ├── SecFloat-Accurate Floating-Point meets Secure 2-Party Computation.pdf ├── The Cost of IEEE Arithmetic in Secure Computation.pdf ├── Trifecta- Faster High-Throughput Three-Party Computation over WAN Using Multi-Fan-In Logic Gates.pdf └── Turbospeedz.pdf ├── 密码学-从入门到放弃 ├── PPA介绍.pdf ├── 中国剩余定理简介 (CRT).pdf └── 为什么秘密分享要在环里计算.pdf └── 机器学习隐私保护 ├── ABY3-A Mixed Protocol Framework for Machine Learning.pdf ├── ASTRA High Throughput 3PC over Rings with Application to Secure Prediction.pdf ├── BumbleBee.pdf ├── CAESAR.pdf ├── CrypTFlow2_ Practical 2-Party Secure Inference.pdf ├── CryptGPU_ Fast Privacy-Preserving Machine Learning on the GPU.pdf ├── DELPHI_ A Cryptographic Inference Service for Neural Networks.pdf ├── FALCON-Honest-Majority Maliciously Secure Framework for Private Deep Learning.pdf ├── FLASH-Fast and Robust Framework for Privacy-Preserving Machine Learning.pdf ├── MPC-based SecAgg.pdf ├── NFGen.pdf ├── Piranha-A GPU Platform for Secure Computation.pdf ├── QUOTIENT_Two-Party Neural Network Training and Prediction.pdf ├── SIRNN-A Math Library for Secure RNN Inference.pdf ├── Scalable Multi-Party Computation Protocols for Machine Learning in the Honest-Majority Setting.pdf ├── SecretFlow-SPU A Performant and User-Friendly Framework.pdf ├── XONN_ XNOR-based Oblivious Deep Neural Network Inference.pdf ├── [ACSAC23]Sec-Softmoid.pdf ├── [NDSS'24] MPCDIFF.pdf ├── [NDSS24] Pencil.pdf └── 横向联邦学习下隐私保护安全聚合：问题，方法，与展望.pdf /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ye-D/PaperNotes-MPC/HEAD/README.md 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