awesome-ppml
github.com/mortendahl/awesome-ppml ↗A curated list of resources for privacy-preserving machine learning
149
GitHub Stars
33
Curated Resources
5
Categories
23 hours ago
Last Refreshed
News and UpdatesSoftwareConferences and WorkshopsTutorials and CoursesResearch Papers
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me research papers resources from awesome-ppml"
Installation instructions →What's inside
Research Papers
- ABY3: A Mixed Protocol Framework for Machine Learning, MR'18
- Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications, RWTSSK'17
- CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs, DSCLLMMM'18
- Chiron: Privacy-preserving Machine Learning as a Service, HSSSW'18
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy, DGLLNW'16
- DeepSecure: Scalable Provably-Secure Deep Learning, RRK'17
Resources
- awesome-differential-privacy
for differential privacy
- awesome-he
for homomorphic encryption
- awesome-mpc
for secure multi-party computation
News and Updates
Conferences and Workshops
Software
- HE Transformer
homomorphic encryption backend for nGraph
- PySyft
encrypted, privacy preserving machine learning in PyTorch and TensorFlow
- TensorFlow Federated
federated learning in TensorFlow
- TensorFlow Privacy
differential privacy in TensorFlow
- TF Encrypted
encrypted machine learning in TensorFlow
Showing a sample of 33 resources. View the full list on GitHub →