Privacy-Preserving-Machine-Learning-Resources

Content

• About
• Secure Machine Learning
• Secure Federated Learning
• Libraries and Frameworks
• General Research
• Blogs

About

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

Secure Machine Learning

• Machine Learning Classification over Encrypted Data, NDSS'14
• Oblivious Multi-Party Machine Learning on Trusted Processors, USENIX SECURITY'16
• Prio: Private, Robust, and Scalable Computation of Aggregate Statistics, NSDI'17
• SecureML: A System for Scalable Privacy-Preserving Machine Learning, S&P'17
• MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, CCS'17
• Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, AsiaCCS'17
• DeepSecure: Scalable Provably-Secure Deep Learning, DAC'17
• Secure Computation for Machine Learning With SPDZ, NIPS'18
• ABY3:a Mixed protocol Framework for Machine Learning, CCS'18
• SecureNN: Efficient and Private Neural Network Training, PETS'18
• Gazelle: A Low Latency Framework for Secure Neural Network Inference, USENIX SECURITY'18
• CHET: an optimizing compiler for fully-homomorphic neural-network inferencing, PLDI'19
• New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning, S&P'19
• FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning, PETS'19
• Helen: Maliciously Secure Coopetitive Learning for Linear Models, S&P'19
• Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. CCS'19
• QUOTIENT: two-party secure neural network training and prediction, CCS'19
• ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction, CCSW'19
• Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning, NDSS'20
• BLAZE: Blazing Fast Privacy-Preserving Machine Learning, NDSS'20
• Delphi: A Cryptographic Inference Service for Neural Networks, USENIX SECURITY'20
• FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
• MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference, ARES'20
• SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search, USENIX Security'20
• PySyft: A Generic Framework for Privacy Preserving Deep Learning
• Private Deep Learning in TensorFlow Using Secure Computation
• CryptoDL: Deep Neural Networks over Encrypted Data
• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
• CrypTFlow: Secure TensorFlow Inference
• CrypTFlow2: Practical 2-Party Secure Inference, CCS'20
• ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
• Practical Privacy-Preserving K-means Clustering, PETS'20
• ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (Full Version), USENIX Security'21
• Secure Evaluation of Quantized Neural Networks, PETS'20
• SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Secure Federated Learning

• Privacy-Preserving Deep Learning, CCS'15
• Practical Secure Aggregation for Privacy Preserving Machine Learning, CCS'17
• Privacy-Preserving Deep Learning via Additively Homomorphic Encryption, TIFS'17
• NIKE-based Fast Privacy-preserving High-dimensional Data Aggregation for Mobile Devices, CACR'18
• PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks, CCSW'19
• VerifyNet: Secure and verifiable federated learning, TIFS'19
• PrivColl: Practical Privacy-Preserving Collaborative Machine Learning
• NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning, TDSC'20
• SAFER: Sparse secure Aggregation for FEderated leaRning
• Secure Byzantine-Robust Machine Learning
• Secure Single-Server Aggregation with (Poly)Logarithmic Overhead, CCS'20
• Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning, USENIX ATC'21
• FedSel: Federated SGD under Local Differential Privacy with Top-k Dimension Selection, DASFAA'20

Libraries and Frameworks

• TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits
• SPDZ-2: Multiparty computation with SPDZ, MASCOT, and Overdrive offline phases
• ABY: A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
• Obliv - C: C compiler for embedding privacy preserving protocols:
• TFHE: Fast Fully Homomorphic Encryption Library over the Torus
• SEAL: Simple Encypted Arithmatic Library
• PySEAL: Python interface to SEAL
• HElib: An Implementation of homomorphic encryption
• EzPC: programmable, efficient, and scalable secure two-party computation for machine learning
• CUDA-accelerated Fully Homomorphic Encryption Library
• CrypTen: A framework for Privacy Preserving Machine Learning
• tf-encrypted: A Framework for Machine Learning on Encrypted Data
• Sharemind

General Research

• Multiparty computation from somewhat homomorphic encryption, Crypto'12
• Practical covertly secure MPC for dishonest majority–or: breaking the SPDZ limits, ESORICS'13
• MASCOT: faster malicious arithmetic secure computation with oblivious transfer, CCS'16
• SPDZ^2k: Efficient MPC mod 2^k for Dishonest Majority, Crypto'18
• Overdrive^2k: Making SPDZ Great Again
• High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority, CCS'16
• Sharemind: A framework for fast privacy-preserving computations, ESORICS'08
• Efficiently Verifiable Computation on Encrypted Data, CCS'14
• Membership inference attacks against machine learning models, S&P'17
• Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning, S&P'19
• Terngrad: Ternary gradients to reduce communication in distributed deep learning, NIPS'17
• MachineLearningwithAdversaries: ByzantineTolerantGradientDescen, NIPS'17

Blogs

• Cryptography and Machine Learning: Mixing both for private data analysis
• Building Safe A.I.: A Tutorial for Encrypted Deep Learning
• Awesome MPC: Curated List of resources for MPC