tf-encrypted
Machine learning framework
Enables secure machine learning computations in TensorFlow without requiring expertise in cryptography or distributed systems.
A Framework for Encrypted Machine Learning in TensorFlow
1k stars
53 watching
215 forks
Language: Python
last commit: about 2 years agoLinked from 2 awesome lists
confidential-computingcryptographydeep-learningmachine-learningprivacysecure-computationtensorflow
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A framework for training and prediction on encrypted data using secure multi-party computation and homomorphic encryption in TensorFlow. | 3 | |
| A secure distributed dataflow framework for encrypted machine learning and data processing | 59 | |
| An end-to-end platform for building and deploying machine learning applications | 186,822 | |
| A Python library for training machine learning models while preserving the privacy of sensitive data | 1,947 | |
| A library that enables distributed deep learning by partitioning tensors across processors in a mesh topology. | 1,597 | |
| This project presents a framework for robust federated learning against backdoor attacks. | 71 | |
| Haskell bindings for a popular machine learning framework, allowing developers to build and deploy neural networks in the Haskell programming language. | 1,583 | |
| A collection of pre-trained machine learning models for use in web applications. | 14,180 | |
| A Python framework for collaborative machine learning without sharing sensitive data | 738 | |
| A Haskell framework for defining and compiling valid deep learning models to external frameworks like TensorFlow JS or Keras. | 101 | |
| An API for utilizing the TensorFlow machine learning framework in Ruby | 829 | |
| A high-level framework for machine intelligence applications using TensorFlow. | 11 | |
| A software framework for using tensor networks to improve machine learning performance | 150 | |
| A framework for privacy-preserving machine learning using fully homomorphic encryption | 1,045 | |
| A framework for applying secure computing techniques to machine learning models without modifying the underlying frameworks. | 1,554 |