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Awesome Secure-Machine Learning Awesome

A curated list of source of secure machine learning. (Feel Free to send a pull request if you have new papers to add.)

Homomorphic Encryption

Survey papers introducing HE:

HE schemes:

  • 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

More advances in HE:

  • 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

HE for private deep neural netowrk inference:

  • 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

HE for private deep neural netowrk training:

  • FHESGD - Towards Deep Neural Network Training on Encrypted Data
  • Glyph - Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

HE for other models:

Multiparty Secure Computation

Garbled Circuit:

  • 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

Function Secret Sharing:

Oblivious Transfer:

  • Asharov et. al. - More Efficient Oblivious Transfer andExtensions for Faster Secure Computation*
  • Ishai et. al. - ExtendingObliviousTransfersE±ciently

MPC for Deep Learning:

  • 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

MPC for other models:

  • SANNS(KNN) - SANNS:Scaling Up Secure Approximatek-Nearest Neighbors Search

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