CrypTen
Secure ML framework
A framework for applying secure computing techniques to machine learning models without modifying the underlying frameworks.
A framework for Privacy Preserving Machine Learning
2k stars
43 watching
280 forks
Language: Python
last commit: about 1 month ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 zama-ai/concrete-ml | A framework for privacy-preserving machine learning using fully homomorphic encryption | 997 |
 ai-secure/crfl | This project presents a framework for robust federated learning against backdoor attacks. | 71 |
 tf-encrypted/tf-encrypted | Enables secure machine learning computations in TensorFlow without requiring expertise in cryptography or distributed systems. | 1,210 |
 microsoft/0xdeca10b | A framework for hosting and training machine learning models on a blockchain, enabling secure sharing and prediction without requiring users to pay for data or model updates. | 556 |
 securefederatedai/openfl | A framework for enabling collaboration on machine learning projects without sharing sensitive data | 728 |
 mortendahl/tf-encrypted | A framework for training and prediction on encrypted data using secure multi-party computation and homomorphic encryption in TensorFlow. | 3 |
 somnibyte/mlkit | A framework for implementing machine learning algorithms in Swift to make it easier for developers to incorporate ML into their projects. | 152 |
 abertaymachinelearninggroup/cryptoknight | A framework for classifying cryptographic primitives using dynamic binary instrumentation and deep learning | 38 |
 ganyuwang/vfl-czofo | A unified framework for improving privacy and reducing communication overhead in distributed machine learning models. | 11 |
 asappresearch/flambe | An ML framework for accelerating research and its integration into production workflows | 262 |
 xtra-computing/privml | An effort to develop and evaluate private machine learning frameworks | 19 |
 openmined/private-ai-resources | A curated collection of resources and libraries for secure machine learning research and development | 471 |
| tf-encrypted/moose | A secure distributed dataflow framework for encrypted machine learning and data processing | 59 |
 samsung/veles | A distributed machine learning platform for rapid Deep learning application development | 906 |
 cryptocat/cryptocat | An application providing end-to-end encrypted communication | 1,097 |