A curated list of source of secure machine learning. (Feel Free to send a pull request if you have new papers to add.)
Survey papers introducing HE:
- A Survey on Homomorphic Encryption Schemes: Theory and Implementation - Introduing the development of HE and also several popular HE schemas.
- Survey on Homomorphic Encryptionand Address of New Trend - With some introduction of HE's application recently.
- Paillier(SomeWhat HE) - Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
- BGV - Fully Homomorphic Encryption without Bootstrapping
- BV - Fully Homomorphic Encryption from Ring-LWE and Security for Key Dependent Messages
- FV - Somewhat Practical Fully HomomorphicEncryption
- GSW - Homomorphic Encryption from Learning with Errors:Conceptually-Simpler, Asymptotically-Faster, Attribute-Based
- TFHE - TFHE: Fast Fully Homomorphic Encryptionover the Torus
- HEAAN(CKKS) - Homomorphic Encryptionfor Arithmetic of Approximate Numbers
- HEAAN with bootstrap -Bootstrapping for ApproximateHomomorphic Encryption
- Takeshita et. al. -Enabling Faster Operations for Deeper Circuitsin Full RNS Variants of FV-like SomewhatHomomorphic Encryption
- Chen et.al. -Efficient Homomorphic ConversionBetween (Ring) LWE Ciphertexts
- CryptoNets - CryptoNets: Applying Neural Networks to Encrypted Datawith High Throughput and Accuracy
- FHE-DiNN - Fast Homomorphic Evaluation ofDeep Discretized Neural Networks
- Faster CryptoNets - Faster CryptoNets: Leveraging Sparsity forReal-World Encrypted Inference
- Chimera - Chimera: a unified framework for B/FV, TFHE and HEAAN fully homomorphic encryption andpredictions for deep learning
- TAPAS - TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
- nGraph-HE - nGraph-HE: A Graph Compiler for Deep Learning onHomomorphically Encrypted Data
- CryptoDL - Low Latency Privacy Preserving Inference
- SHE -SHE: A Fast and Accurate Deep Neural Network forEncrypted Data
- ZAMA -New Challenges for Fully Homomorphic Encryption
- FHESGD - Towards Deep Neural Network Training on Encrypted Data
- Glyph - Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
- Hardy et. al.(Logistic Regression) - Private federated learning on vertically partitioned datavia entity resolution and additively homomorphicencryption
- Crawford et. al. (Logistic Regression) - Doing Real Work with FHE: The Case of Logistic Regression
- SecureBoost (XGBoost) - SecureBoost: A Lossless Federated Learning Framework
- Cheon et. al. (Cluster) - Towards a Practical Cluster Analysisover Encrypted Data
- Jaschke et. al. (Cluster) - Unsupervised Machine Learning on Encrypted Data
- Han et. al. (Cluster) - Efficient Logistic Regression on Large EncryptedData
- Li et. al. (Distributed Logistic Regression) - Faster Secure Data Mining via Distributed Homomorphic Encryption
- Sameer Narahari Wagh - New Directions in EfficientPrivacyPreserving Machine Learning
- Yao - Protocols for Secure Computations
- FairplayMP - FairplayMP: a system for secure multi-party computation
- Kolesnikov et. al. - Improved Garbled Circuit: Free XOR Gates andApplications
- Mohassel et. al. - Fast and Secure Three-party Computation:The Garbled Circuit Approach
- Zahur et. al. - Two Halves Make a WholeReducing Data Transfer in Garbled Circuits using Half Gates
- Boyle et. al. - Function Secret Sharing
- Boyle et. al. - Function Secret Sharing: Improvements and Extensions.
- Boyle et. al. - Secure Computation with Preprocessing viaFunction Secret Sharing
- Asharov et. al. - More Efficient Oblivious Transfer andExtensions for Faster Secure Computation*
- Ishai et. al. - ExtendingObliviousTransfersE±ciently
- SecureML - SecureML: A System for Scalable Privacy-PreservingMachine Learning
- EzPC - EzPC: Programmable, Efficient, and ScalableSecure Two-Party Computation for Machine Learning
- GAZELLE - GAZELLE: A Low Latency Framework for SecureNeural Network Inference
- ABY3 - ABY3: A Mixed Protocol Framework for Machine Learning
- SecureNN - SecureNN: 3-Party Secure Computation forNeural Network Training
- XONN - XONN:XNOR-based Oblivious Deep Neural Network Inference
- Flash - FLASH: Fast and Robust Framework forPrivacy-preserving Machine Learning
- ASTRA - ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction
- Trident - Trident: Efficient 4PC Framework for PrivacyPreserving Machine Learning
- BLAZE - BLAZE: Blazing Fast Privacy-Preserving MachineLearning
- DELPHI - DELPHI: A Cryptographic Inference Service for Neural Networks
- FALCON - FALCON: Honest-Majority Maliciously Secure Frameworkfor Private Deep Learning
- AriaNN - ARIANN: Low-Interaction Privacy-PreservingDeep Learning via Function Secret Sharing
- SWIFT - SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
- SANNS(KNN) - SANNS:Scaling Up Secure Approximatek-Nearest Neighbors Search