Private machine learning progress
This is a curated list of resources related to the research and development of private machine learning.
- Gazelle: A Low Latency Framework for Secure Neural Network Inference, January 16, 2018
- Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, November 29, 2017
- CryptoDL: Deep Neural Networks over Encrypted Data, November 14, 2017
- MiniONN: Oblivious Neural Network Predictions via MiniONN Transformations, November 3, 2017
- DeepSecure: Scalable Provably-Secure Deep Learning, May 24, 2017
- SecureML: A System for Scalable Privacy-Preserving Machine Learning, April 19, 2017
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, February 24, 2016
- TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits
- SPDZ-2: Multiparty computation with SPDZ and MASCOT offline phase
- Obliv - C: C compiler for embedding privacy preserving protocols:
- TFHE: Fast Fully Homomorphic Encryption Library over the Torus
- SEAL: Simiple Encypted Arithmatic Library
- PySEAL: Python interface to SEAL
- HElib: An Implementation of homomorphic encryption
- Overdrive: Making SPDZ Great Again
- Privacy-Preserving Logistic Regression Training
- Between a Rock and a Hard Place: Interpolating Between MPC and FHE
- Privacy-Preserving Boosting with Random Linear Classifiers for Learning from User-Generated Data
- The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
- Improvements for Gate-Hiding Garbled Circuits
- Practical Secure Aggregation for Privacy-Preserving Machine Learning
- CryptoRec: Secure Recommendations as a Service
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Differentially Private Generative Adversarial Network
- Doing Real Work with FHE: The Case of Logistic Regression
- ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data
- Scalable Private Learning with PATE
- Doing Real Work with FHE: The Case of Logistic Regression
- Reading in the Dark: Classifying Encrypted Digits with Functional Encryption
- Stealing Hyperparameters in Machine Learning
- Cryptography and Machine Learning: Mixing both for private data analysis
- Building Safe A.I.: A Tutorial for Encrypted Deep Learning
Thanks to members of the OpenMined community who have shared links on slack: @morgangiraud, @jvmancuso
If you have any links to add please send a pull request, and we'll take a look. There is so much happening in this space!