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Materials about Privacy-Preserving Machine Learning
https://github.com/Ye-D/PPML-Resource
deep-learning machine-learning mpc privacy secure-computation
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Materials about Privacy-Preserving Machine Learning
Host: GitHub
URL: https://github.com/Ye-D/PPML-Resource
Owner: Ye-D
Created: 2019-05-22T01:32:33.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2024-06-26T15:15:06.000Z (7 months ago)
Last Synced: 2024-08-01T18:30:12.916Z (6 months ago)
Topics: deep-learning, machine-learning, mpc, privacy, secure-computation
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Watchers: 12
Forks: 51
Open Issues: 0
Metadata Files:
- Readme: ReadMe.md
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README

        # Privacy-Preserving-Machine-Learning-Resources

## Content

- [Privacy-Preserving-Machine-Learning-Resources](#privacy-preserving-machine-learning-resources)

  - [Content](#content)

- [About](#about)

- [Secure Machine Learning](#secure-machine-learning)

- [MPC](#mpc)

- [Federated Learning](#federated-learning)

  - [Secure Federated Learning](#secure-federated-learning)

  - [Communication Optimization](#communication-optimization)

  - [Byzantine-Tolerant](#byzantine-tolerant)

- [Privacy Leakages of ML/FL](#privacy-leakages-of-mlfl)

- [Blogs](#blogs)

- [Libraries and Frameworks](#libraries-and-frameworks)

# About

This is a current list of resources related to the research and development of privacy-preserving machine learning.

# Secure Machine Learning

* [安全多方计算及其在机器学习中的应用, 计算机研究与发展'21](https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2021.20210626)

* [Machine Learning Classification over Encrypted Data, NDSS'14](https://eprint.iacr.org/2014/331.pdf)

* [Oblivious Multi-Party Machine Learning on Trusted Processors, USENIX SECURITY'16](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/ohrimenko)

* [SecureML: A System for Scalable Privacy-Preserving Machine Learning, S&P'17](https://eprint.iacr.org/2017/396)

* [MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, CCS'17](https://acmccs.github.io/papers/p619-liuA.pdf)

* [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, AsiaCCS'17](https://eprint.iacr.org/2017/1164)

* [DeepSecure: Scalable Provably-Secure Deep Learning, DAC'17](https://arxiv.org/abs/1705.08963)

* [Secure Computation for Machine Learning With SPDZ, NIPS'18](https://arxiv.org/abs/1901.00329)

* [ABY3:a Mixed protocol Framework for Machine Learning, CCS'18](https://eprint.iacr.org/2018/403.pdf)

* [SecureNN: Efficient and Private Neural Network Training, PoPETs'18](https://eprint.iacr.org/2018/442.pdf)

* [Gazelle: A Low Latency Framework for Secure Neural Network Inference, USENIX SECURITY'18](https://arxiv.org/abs/1801.05507)

* [CHET: an optimizing compiler for fully-homomorphic neural-network inferencing, PLDI'19](https://dl.acm.org/citation.cfm?id=3314628)

* [New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning, S&P'19](https://eprint.iacr.org/2019/599.pdf)

* [Helen: Maliciously Secure Coopetitive Learning for Linear Models, S&P'19](https://ieeexplore.ieee.org/abstract/document/8835215)

* [Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. CCS'19](https://dl.acm.org/citation.cfm?id=3363207)

* [XONN: XNOR-based Oblivious Deep Neural Network Inference, USENIX Security'19](https://www.usenix.org/conference/usenixsecurity19/presentation/riazi)

* [QUOTIENT: two-party secure neural network training and prediction, CCS'19](https://dl.acm.org/citation.cfm?id=3339819)

* [Secure Evaluation of Quantized Neural Networks, PoPETs'20](https://content.sciendo.com/view/journals/poPoPETs/2020/4/article-p355.xml)

* [ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction, CCSW'19](https://eprint.iacr.org/2019/429)

* [SoK: Modular and Efficient Private Decision Tree Evaluation, PoPETs'19](https://eprint.iacr.org/2018/1099.pdf)

* [Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20](https://eprint.iacr.org/2019/1315)

* [BLAZE: Blazing Fast Privacy-Preserving Machine Learning, NDSS'20](https://eprint.iacr.org/2020/042)

* [FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning, PoPETs'20](https://eprint.iacr.org/2019/1365)

* [Delphi: A Cryptographic Inference Service for Neural Networks, USENIX SECURITY'20](https://eprint.iacr.org/2020/050)

* [ParSecureML: An Efficient Parallel Secure Machine Learning Framework on GPUs, ICPP'20](https://dl.acm.org/doi/abs/10.1145/3404397.3404399)

* [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning, PoPETs'21](https://arxiv.org/abs/2004.02229)

* [MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference, ARES'20](https://dl.acm.org/doi/abs/10.1145/3407023.3407045)

* [SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search, USENIX Security'20](https://www.usenix.org/conference/usenixsecurity20/presentation/chen-hao)

* [PySyft: A Generic Framework for Privacy Preserving Deep Learning](https://arxiv.org/abs/1811.04017)

* [Private Deep Learning in TensorFlow Using Secure Computation](https://arxiv.org/abs/1810.08130)

* [CryptoDL: Deep Neural Networks over Encrypted Data](https://arxiv.org/abs/1711.05189)

* [CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/04/CryptonetsTechReport.pdf)

* [CrypTFlow: Secure TensorFlow Inference](https://eprint.iacr.org/2019/1049.pdf)

* [CrypTFlow2: Practical 2-Party Secure Inference, CCS'20](https://arxiv.org/abs/2010.06457)

* [ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing](https://arxiv.org/abs/2006.04593)

* [Practical Privacy-Preserving K-means Clustering, PoPETs'20](https://content.sciendo.com/view/journals/poPoPETs/2020/4/article-p414.xml)

* [SOTERIA: In Search of Efficient Neural Networks for Private Inference, 20](https://arxiv.org/abs/2007.12934)

* [SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning, USENIX Security'21](https://arxiv.org/abs/2005.10296)

* [An Efficient 3-Party Framework for Privacy-Preserving Neural Network Inference, ESORICS'20](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_21)

* [Secure and Verifiable Inference in Deep Neural Networks, ACSAC'20](https://dl.acm.org/doi/abs/10.1145/3427228.3427232)

* [Privacy-preserving Density-based Clustering, AisaCCS'21](https://www.eurecom.fr/publication/6475/download/sec-publi-6475.0.pdf)

* [SIRNN: A Math Library for Secure RNN Inference, S&P'21](https://eprint.iacr.org/2021/459)

* [Let’s Stride Blindfolded in a Forest: Sublinear Multi-Client Decision Trees Evaluation, NDSS'21](https://www.ndss-symposium.org/ndss-paper/lets-stride-blindfolded-in-a-forest-sublinear-multi-client-decision-trees-evaluation/)

* [MUSE: Secure Inference Resilient to Malicious Clients, USENIX Security'21](https://people.eecs.berkeley.edu/~raluca/MUSEcamera.pdf)

* [DeepReDuce: ReLU Reduction for Fast Private Inference, ICML'21](https://arxiv.org/abs/2103.01396)

* [Garbled Neural Networks are Practical](https://eprint.iacr.org/2019/338.pdf)

* [GForce : GPU-Friendly Oblivious and Rapid Neural Network Inference, USENIX Security'21](https://www.usenix.org/conference/usenixsecurity21/presentation/ng)

* [CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU, S&P'21](http://arxiv.org/abs/2104.10949)

* [GALA : Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks, NDSS'21](https://www.ndss-symposium.org/ndss-paper/gala-greedy-computation-for-linear-algebra-in-privacy-preserved-neural-networks/)

* [Fantastic Four: Honest-Majority Four-Party Secure Computation With Malicious Security, USENIX Security'21](https://www.usenix.org/system/files/sec21fall-dalskov.pdf)

* [When homomorphic encryption marries secret sharing: secure large-scale sparse logistic regression and applications in risk control, KDD'21](https://arxiv.org/abs/2008.08753)

* [Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data, NeurIPS'20](https://arxiv.org/pdf/1911.07101.pdf)

* [Mystique: Efficient Conversions for Zero-Knowledge Proofs with Applications to Machine Learning, USENIX Security'21](https://eprint.iacr.org/2021/730)

* [SoK: Efficient Privacy-preserving Clustering, PoPETs'21](https://eprint.iacr.org/2021/809)

* [ZEN: Efficient Zero-Knowledge Proofs for Neural Networks](https://eprint.iacr.org/2021/087/20210127:132648)

* [zkCNN: Zero Knowledge Proofs for Convolutional Neural Network Predictions and Accuracy, CCS'21](https://eprint.iacr.org/2021/673)

* [Secure Quantized Training for Deep Learning, ICML](https://arxiv.org/abs/2107.00501)

* [Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning, USENIX Security'21](https://www.usenix.org/conference/usenixsecurity21/presentation/zheng)

* [Tetrad: Actively Secure 4PC for Secure Training and Inference, NDSS'22](https://arxiv.org/abs/2106.02850)

* [Adam in Private : Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation, PoPETs'22](https://arxiv.org/abs/2106.02203)

* [SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/chandran)

* [Circa : Stochastic ReLUs for Private Deep Learning, NeurIPS'21](https://proceedings.neurips.cc/paper/2021/file/11eba2991cc62daa4a85be5c0cfdae97-Paper.pdf)

* [Banners: Binarized Neural Networks with Replicated Secret Sharing, IH&MMSec'21](https://dl.acm.org/doi/10.1145/3437880.3460394)

* [Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference, USENIX Security'22](https://eprint.iacr.org/2022/207)

* [Secure Poisson Regression, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/kelkar)

* [SecFloat: Accurate Floating-Point meets Secure 2-Party Computation, S&P'22](https://eprint.iacr.org/2022/322)

* [MPClan: Protocol Suite for Privacy-Conscious Computations, IACR ePrint'22](https://eprint.iacr.org/2022/675)

* [LLAMA: A Low Latency Math Library for Secure Inference, PoPETs'22](https://eprint.iacr.org/2022/793)

* [Pika: Secure Computation using Function Secret Sharing over Rings, PoPETs'22](https://eprint.iacr.org/2022/826)

* [Piranha: A GPU Platform for Secure Computation, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/watson)

* [Efficient Secure Three-Party Sorting with Applications to Data Analysis and Heavy Hitters, CCS'22](https://eprint.iacr.org/2019/695)

* [Private and Reliable Neural Network Inference, CCS'22](https://files.sri.inf.ethz.ch/website/papers/ccs22-phoenix.pdf)

* [SortingHat: Efficient Private Decision Tree Evaluation via Homomorphic Encryption and Transciphering, CCS'22](https://eprint.iacr.org/2022/757)

* [Iron: Private Inference on Transformers, NeurIPS '22](https://openreview.net/forum?id=deyqjpcTfsG)

* [Private and Reliable Neural Network Inference, CCS'22](https://files.sri.inf.ethz.ch/website/papers/ccs22-phoenix.pdf)

* [SecureBiNN: 3-Party Secure Computation for Binarized Neural Network Inference, ESORICS'22](https://link.springer.com/chapter/10.1007/978-3-031-17143-7_14)

* [Meteor: Improved Secure 3-Party Neural Network Inference with Reducing Online Communication Costs, WWW'23](https://dl.acm.org/doi/abs/10.1145/3543507.3583272)

* [Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy, S&P'23](https://www.computer.org/csdl/proceedings-article/sp/2023/933600a076/1He7XMLcnsc)

* [Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning, S&P'23](https://www.computer.org/csdl/proceedings-article/sp/2023/933600b295/1Js0DPq2AqQ)

* [Fusion: Efficient and Secure Inference Resilient to Malicious Servers, NDSS'23](https://www.ndss-symposium.org/ndss-paper/fusion-efficient-and-secure-inference-resilient-to-malicious-servers/)

* [REDsec: Running Encrypted Discretized Neural Networks in Seconds, NDSS'23](https://www.ndss-symposium.org/ndss-paper/redsec-running-encrypted-discretized-neural-networks-in-seconds/)

* [SoK: Cryptographic Neural-Network Computation, S&P'23](https://sokcryptonn.github.io/)

* [Secure Floating-Point Training, USENIX Security'23](https://www.usenix.org/system/files/sec23fall-prepub-212-rathee.pdf)

* [Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree, USENIX Security'23](https://www.usenix.org/conference/usenixsecurity23/presentation/lu)

* [Efficient 3PC for Binary Circuits with Application to Maliciously-Secure DNN Inference, USENIX Security'23](https://www.usenix.org/biblio-13767)

* [Level Up: Private Non-Interactive Decision Tree Evaluation using Levelled Homomorphic Encryption, CCS'23](https://cs.paperswithcode.com/paper/level-up-private-non-interactive-decision)

* [PUMA: Secure Inference of LLaMA-7B in Five Minutes](https://arxiv.org/abs/2307.12533)

* [Orca: FSS-based Secure Training and Inference with GPUs, S&P'24](https://www.computer.org/csdl/proceedings-article/sp/2024/313000a063/1RjEaAAmAAE)

* [SIGMA: Secure GPT Inference with Function Secret Sharing, PoPTEs'24](https://www.microsoft.com/en-us/research/publication/sigma-secure-gpt-inference-with-function-secret-sharing/)

* [CipherGPT: Secure Two-Party GPT Inference](https://eprint.iacr.org/2023/1147)

* [HELiKs: HE Linear Algebra Kernels for Secure Inference, CCS'23](https://dl.acm.org/doi/10.1145/3576915.3623136)

* [MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models, NDSS'24](https://www.ndss-symposium.org/ndss-paper/mpcdiff-testing-and-repairing-mpc-hardened-deep-learning-models/)

* [Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions, USENIX Security'24](https://www.usenix.org/system/files/sec24summer-prepub-373-diaa.pdf)

* [MD-ML: Super Fast Privacy-Preserving Machine Learning for Malicious Security with a Dishonest Majority, USENIX Security'24](https://www.usenix.org/conference/usenixsecurity24/presentation/yuan)

# MPC

* [实用安全多方计算协议关键技术研究进展, 计算机研究与发展'15](https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2015.20150763)

* [Scalable and unconditionally secure multiparty computation, Crypto'07](https://www.iacr.org/archive/crypto2007/46220565/46220565.pdf)

* [Sharemind: A framework for fast privacy-preserving computations, ESORICS'08](https://link.springer.com/chapter/10.1007/978-3-540-88313-5_13)

* [Secure computation with fixed-point numbers, FC'10](https://link.springer.com/chapter/10.1007/978-3-642-14577-3_6)

* [Multiparty computation from somewhat homomorphic encryption, Crypto'12](https://link.springer.com/chapter/10.1007/978-3-642-32009-5_38)

* [Practical covertly secure MPC for dishonest majority–or: breaking the SPDZ limits, ESORICS'13](https://link.springer.com/chapter/10.1007/978-3-642-40203-6_1)

* [GMW vs. Yao? Efficient secure two-party computation with low depth circuits, FC'13](https://link.springer.com/chapter/10.1007/978-3-642-39884-1_23)

* [Efficiently Verifiable Computation on Encrypted Data, CCS'14](https://dl.acm.org/citation.cfm?id=2660366)

* [ABY: A Framework for Efficient Mixed-Protocol Secure Two-Party Computation, NDSS'15](https://encrypto.de/papers/DSZ15.pdf)

* [MASCOT: faster malicious arithmetic secure computation with oblivious transfer, CCS'16](https://dl.acm.org/citation.cfm?id=2978357)

* [High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority, CCS'16](https://dl.acm.org/citation.cfm?id=2978331)

* [High- throughput secure three-party computation for malicious adversaries and an honest majority, Crypto'17](https://eprint.iacr.org/2016/944.pdf)

* [A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority, CCS'17](https://eprint.iacr.org/2017/816.pdf)

* [Overdrive^2k: Making SPDZ Great Again, Eurocrypto'18](https://eprint.iacr.org/2017/1230)

* [SPDZ^2k: Efficient MPC mod 2^k for Dishonest Majority, Crypto'18](https://link.springer.com/chapter/10.1007/978-3-319-96881-0_26)

* [Secure outsourced matrix computation and application to neural networks, CCS'18](https://dl.acm.org/doi/10.1145/3243734.3243837)

* [Fast large-scale honest-majority MPC for malicious adversaries, Crypto'18](https://eprint.iacr.org/2018/570)

* [Minimising communication in honest-majority MPC by batchwise multiplication verification, ACNS'18](https://eprint.iacr.org/2018/474)

* [PrivPy: General and Scalable Privacy-Preserving Data Mining, KDD'19](https://dl.acm.org/doi/abs/10.1145/3292500.3330920)

* [Two-thirds honest-majority MPC for malicious adversaries at almost the cost of semi-honest, CCS'19](https://dl.acm.org/doi/10.1145/3319535.3339811)

* [Turbospeedz: Double your online SPDZ! Improving SPDZ using function dependent preprocessing, ACNS'19](https://eprint.iacr.org/2019/080)

* [MP-SPDZ: A Versatile Framework for Multi-Party Computation, CCS'20](https://dl.acm.org/doi/10.1145/3372297.3417872)

* [Senate: A Maliciously-Secure MPC Platform for Collaborative Analytics, USENIX Security'20](https://www.usenix.org/conference/usenixsecurity21/presentation/poddar)

* [Improved primitives for mpc over mixed arithmetic-binary circuits, CRYPTO'20](https://eprint.iacr.org/2020/338.pdf)

* [Malicious Security Comes Free in Honest-Majority MPC, ePrint'20](https://eprint.iacr.org/2020/134)

* [MOTION - A Framework for Mixed-Protocol Multi-Party Computation, TOPS'21](https://eprint.iacr.org/2020/1137)

* [ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (Full Version), USENIX Security'21](https://eprint.iacr.org/2020/1225.pdf)

* [SynCirc: Efficient Synthesis of Depth-Optimized Circuits for Secure Computation, HOST'21](https://encrypto.de/papers/PSSY21HOST.pdf)

* [MAGE: Nearly Zero-Cost Virtual Memory for Secure Computation, USENIX OSDI'21](https://www.usenix.org/conference/osdi21/presentation/kumar)

* [VASA : Vector AES Instructions for Security Applications, ACSAC'21](https://eprint.iacr.org/2021/1493.pdf)

* [ATLAS: Efficient and Scalable MPC in the Honest Majority Setting, CRYPTO'21](https://eprint.iacr.org/2021/833)

* [The Cost of IEEE Arithmetic in Secure Computation, LatinCrypt'21](https://eprint.iacr.org/2021/054)

* [Fast Fully Secure Multi-Party Computation over Any Ring with Two-Thirds Honest Majority, CCS'22](https://eprint.iacr.org/2022/623)

* [NFGen: Automatic Non-linear Function Evaluation Code Generator for General-purpose MPC Platforms, CCS'22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html)

* [PentaGOD: Stepping beyond Traditional GOD with Five Parties, CCS'22](https://eprint.iacr.org/2022/1118)

* [TurboPack: Honest Majority MPC with Constant Online Communication, CCS'22](https://www.sigsac.org/ccs/CCS2022/program/accepted-papers.html)

* [Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics, CCS'22](https://arxiv.org/abs/2107.12407)

* [To Trust or Not to Trust: Hybrid Multi-party Computation with Trusted Execution Environment, NDSS'22](https://www.ndss-symposium.org/ndss-paper/auto-draft-222/)

* [Binary Search in Secure Computation, NDSS'22](https://eprint.iacr.org/2021/1049)

* [More Efficient Dishonest Majority Secure Computation over $\mathbb{Z}_{2^k}$ via Galois Rings, Crypto'22](https://eprint.iacr.org/2022/815)

* [Sharing Transformation and Dishonest Majority MPC with Packed Secret Sharing, Crypto'22](https://eprint.iacr.org/2022/831)

* [Le Mans: Dynamic and Fluid MPC for Dishonest Majority, Crypto'22](https://eprint.iacr.org/2021/1579)

* [Round-Optimal and Communication-Efficient Multiparty Computation, EUROCRYPT'22](https://eprint.iacr.org/2020/1437)

* [Practical Non-interactive Publicly Verifiable Secret Sharing with Thousands of Parties, EUROCRYPT'22](https://eprint.iacr.org/2021/1397)

* [Highly Efficient OT-Based Multiplication Protocols, EUROCRYPT'22](https://eprint.iacr.org/2021/1373.pdf)

* [Batch-OT with Optimal Rate, EUROCRYPT'22](https://www.iacr.org/cryptodb/data/paper.php?pubkey=31828)

* [Round-Optimal Multi-Party Computation with Identifiable Abort, EUROCRYPT'22](https://eprint.iacr.org/2022/645)

* [Secure Multiparty Computation with Free Branching, EUROCRYPT'22](https://www.iacr.org/cryptodb/data/paper.php?pubkey=31942)

* [Secure Multiparty Computation with Sublinear Preprocessing, EUROCRYPT'22](https://iacr.org/cryptodb/data/paper.php?pubkey=31948)

* [Attaining GOD Beyond Honest Majority With Friends and Foes, Asiacrypt'22](https://eprint.iacr.org/2022/1207)

* [Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications, PoPETs'22](https://petsymposium.org/popets/2022/popets-2022-0020.pdf)

* [Silph: A Framework for Scalable and Accurate Generation of Hybrid MPC Protocols, S&P'23](https://www.computer.org/csdl/proceedings-article/sp/2023/933600b796/1Js0Em52Wn6)

* [Faster Secure Comparisons with Offline Phase for Efficient Private Set Intersection, NDSS'23](https://www.ndss-symposium.org/ndss-paper/faster-secure-comparisons-with-offline-phase-for-efficient-private-set-intersection/)

* [FLUTE: Fast and Secure Lookup Table Evaluations, S&P'23](https://eprint.iacr.org/2023/499)

* [Linear Communication in Malicious Majority MPC, CCS'23](https://eprint.iacr.org/2022/781)

* [Grotto: Screaming fast (2+1)-PC or $\mathbb{Z}_{2^n}$ via (2,2)-DPFs, CCS'23](https://eprint.iacr.org/2023/108)

* [COMBINE: COMpilation and Backend-INdependent vEctorization for Multi-Party Computation, CCS'23](https://dl.acm.org/doi/abs/10.1145/3576915.3623181)

* [Don’t Eject the Impostor: Fast Three-Party Computation With a Known Cheater, IEEE S&P'24](https://eprint.iacr.org/2023/1744)

* [Scalable Mixed-Mode MPC, IEEE S&P'24](https://eprint.iacr.org/2023/1700)

# Federated Learning

## Secure Federated Learning

* [Privacy-Preserving Deep Learning, CCS'15](https://dl.acm.org/citation.cfm?id=2813687)

* [Prio: Private, Robust, and Scalable Computation of Aggregate Statistics, NSDI'17](https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/corrigan-gibbs)

* [Practical Secure Aggregation for Privacy Preserving Machine Learning, CCS'17](https://eprint.iacr.org/2017/281.pdf)

* [Privacy-Preserving Deep Learning via Additively Homomorphic Encryption, TIFS'17](https://ieeexplore.ieee.org/document/8241854)

* [NIKE-based Fast Privacy-preserving High-dimensional Data Aggregation for Mobile Devices, CACR'18](http://cacr.uwaterloo.ca/techreports/2018/cacr2018-10.pdf)

* [PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks, CCSW'19](https://eprint.iacr.org/2019/979.pdf)

* [VerifyNet: Secure and verifiable federated learning, TIFS'19](https://ieeexplore.ieee.org/abstract/document/8765347)

* [PrivColl: Practical Privacy-Preserving Collaborative Machine Learning](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_20)

* [NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning, TDSC'20](https://ieeexplore.ieee.org/abstract/document/8981947)

* [SAFER: Sparse secure Aggregation for FEderated leaRning](https://arxiv.org/abs/2007.14861)

* [Secure Byzantine-Robust Machine Learning](https://arxiv.org/abs/2006.04747)

* [Secure Single-Server Aggregation with (Poly)Logarithmic Overhead, CCS'20](https://eprint.iacr.org/2020/704.pdf)

* [Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning, USENIX ATC'21](https://www.usenix.org/conference/atc20/presentation/zhang-chengliang)

* [FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection, DASFAA'20](https://arxiv.org/abs/2003.10637)

* [FLGUARD: Secure and Private Federated Learning, Cryptology Eprint'21](https://eprint.iacr.org/2021/025)

* [Biscotti: A Blockchain System for Private and Secure Federated Learning, TPDS'21](https://ieeexplore.ieee.org/document/9292450)

* [POSEIDON: Privacy-Preserving Federated Neural Network Learning, NDSS'21](https://arxiv.org/abs/2009.00349)

* [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments, MobiSys'21](https://arxiv.org/abs/2104.14380)

* [EIFFeL: Ensuring Integrity for Federated Learning, CCS'22](https://arxiv.org/abs/2112.12727)

* [Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors, USENIX Security'22](https://www.usenix.org/conference/usenixsecurity22/presentation/stevens)

* [Prio+: Privacy Preserving Aggregate Statistics via Boolean Shares, SCN'22](https://link.springer.com/chapter/10.1007/978-3-031-14791-3_23)

* [Local and Central Differential Privacy for Robustness and Privacy in Federated Learning, NDSS'22](https://arxiv.org/abs/2009.03561)

* [ELSA: Secure Aggregation for Federated Learning with Malicious Actors, S&P'23](https://eprint.iacr.org/2022/1695)

* [Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning, SP'23](https://eprint.iacr.org/2023/486)

* [RoFL: Robustness of Secure Federated Learning, SP'23](https://arxiv.org/abs/2107.03311)

## Communication Optimization

* [Terngrad: Ternary gradients to reduce communication in distributed deep learning, NIPS'17](http://papers.nips.cc/paper/6749-terngrad-ternary-gradients-to-reduce-communication-in-distributed-deep-learning)

* [The Convergence of Sparsified Gradient Methods, NIPS'18](https://papers.nips.cc/paper/2018/hash/314450613369e0ee72d0da7f6fee773c-Abstract.html)

## Byzantine-Tolerant

* [Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent, NIPS'17](http://papers.nips.cc/paper/6617-machine-learning-with-adversaries-byzantine-tolerant-gradient-descent)

* [Byzantine stochastic gradient descent, NIPS'18](https://papers.nips.cc/paper/2018/file/a07c2f3b3b907aaf8436a26c6d77f0a2-Paper.pdf)

* [The Hidden Vulnerability of Distributed Learning in Byzantium, ICML'18](https://arxiv.org/abs/1802.07927)

* [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates, ICML'18](https://arxiv.org/abs/1803.01498)

* [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning, USENIX Security'20](https://www.usenix.org/conference/usenixsecurity20/presentation/fang)

* [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping, NDSS'21](https://arxiv.org/abs/2012.13995)

* [Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning, NDSS'21](https://www.ndss-symposium.org/ndss-paper/manipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning/)

* [Justinian’s GAAvernor: Robust Distributed Learning with Gradient Aggregation Agent, USENIX Security'20](https://www.usenix.org/system/files/sec20-pan.pdf)

* [Byzantine-robust and privacy-preserving framework for FEDML, ICLR Workshop'21](https://arxiv.org/pdf/2105.02295.pdf)

* [Learning from History for Byzantine Robust Optimization, ICML'21](http://proceedings.mlr.press/v139/karimireddy21a/karimireddy21a.pdf)

* [FLAME: Taming backdoors in federated learning, USENIX Security'22](https://arxiv.org/abs/2101.02281)

* [BayBFed: Bayesian Backdoor Defense for Federated Learning, SP'23](https://arxiv.org/abs/2301.09508)

# Privacy Leakages of ML/FL

* [Membership inference attacks against machine learning models, S&P'17](https://ieeexplore.ieee.org/abstract/document/7958568)

* [Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning, S&P'19](https://ieeexplore.ieee.org/abstract/document/8835245)

* [Data Poisoning Attacks Against Federated Learning Systems, ESORICS'20](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_24)

* [A Framework for Evaluating Client Privacy Leakages in Federated Learning, ESORICS'20](https://link.springer.com/chapter/10.1007/978-3-030-58951-6_27)

* [A Critical Overview of Privacy in Machine Learning, IEEE Security & Privacy'21](https://www.computer.org/csdl/magazine/sp/2021/04/09433648/1tHMTWXyaUE)

* [Enhanced Membership Inference Attacks against Machine Learning Models, CCS'22](https://arxiv.org/abs/2111.09679)

# Blogs

* [Cryptography and Machine Learning: Mixing both for private data analysis](https://mortendahl.github.io/)

* [Building Safe A.I.: A Tutorial for Encrypted Deep Learning](https://iamtrask.github.io/2017/03/17/safe-ai/)

* [Awesome MPC: Curated List of resources for MPC](https://github.com/rdragos/awesome-mpc)

* [机器学习隐私保护](https://www.zhihu.com/column/c_1121838720017338368)

# Libraries and Frameworks

* [TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits](https://github.com/esonghori/TinyGarble)

* [SPDZ-2: Multiparty computation with SPDZ, MASCOT, and Overdrive offline phases](https://github.com/bristolcrypto/SPDZ-2)

* [ABY: A Framework for Efficient Mixed-Protocol Secure Two-Party Computation](https://github.com/encryptogroup/aby)

* [Obliv - C: C compiler for embedding privacy preserving protocols:](http://oblivc.org/)

* [TFHE: Fast Fully Homomorphic Encryption Library over the Torus](https://github.com/tfhe/tfhe)

* [SEAL: Simple Encypted Arithmatic Library](https://www.microsoft.com/en-us/research/project/simple-encrypted-arithmetic-library/)

* [PySEAL: Python interface to SEAL](https://github.com/Lab41/PySEAL)

* [HElib: An Implementation of homomorphic encryption](https://github.com/shaih/HElib])

* [EzPC: programmable, efficient, and scalable secure two-party computation for machine learning](https://github.com/mpc-msri/EzPC)

* [CUDA-accelerated Fully Homomorphic Encryption Library](https://github.com/vernamlab/cuFHE)

* [CrypTen: A framework for Privacy Preserving Machine Learning](https://github.com/facebookresearch/CrypTen)

* [tf-encrypted: A Framework for Machine Learning on Encrypted Data](https://github.com/tf-encrypted/tf-encrypted)

* [Sharemind](https://sourceforge.net/projects/sharemind/)

* [PythonPaillier](https://github.com/data61/python-paillier)

* [TenSEAL](https://github.com/OpenMined/TenSEAL)

* [MP-SPDZ](https://github.com/data61/MP-SPDZ)

* [Securenn-public](https://github.com/snwagh/securenn-public)

* [SecMML](https://github.com/FudanMPL/SecMML)

* [mnist-mpc](https://github.com/csiro-mlai/mnist-mpc)

* [Private-Set-Intersection](https://github.com/bit-ml/Private-Set-Intersection)

* [falcon-public](https://github.com/snwagh/falcon-public)

* [Rosetta](https://github.com/LatticeX-Foundation/Rosetta)

* [Antchain-MPC](https://github.com/alipay/Antchain-MPC)

* [Kunlun](https://github.com/yuchen1024/Kunlun)

* [MOTION2NX](https://github.com/encryptogroup/MOTION2NX)

* [SecureQ8](https://github.com/anderspkd/SecureQ8)

* [mpc-benchmarks](https://github.com/mkskeller/mpc-benchmarks)

* [muse](https://github.com/mc2-project/muse)

* [Primihub](https://github.com/primihub/primihub)

* [concrete](https://github.com/zama-ai/concrete)

* [SecretFlow](https://github.com/secretflow/secretflow)