Private machine learning progress
This is a curated list of resources related to the research and development of private machine learning.
- 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