Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
-
Updated
Jun 10, 2024 - Jupyter Notebook
Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
Extension of the MOTION2NX framework to implement neural network inferencing task where the data is supplied to the “secure compute servers” by the “data providers”.
Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
Learn how to apply core privacy principles and techniques to the data science and machine learning workflows with Python open source libraries for privacy-preserving machine learning.
Sisyphus: A Cautionary Tale of Using Polynomial Activations in Privacy-Preserving Deep Learning
Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022
A compiled list of resources and materials for PPML
A Learning Journal on (Privacy-Preserving) AI for Medicine and Healthcare
A Replication (and Tribute) of The Log of Gravity
A C++-based framework for privacy-preserving machine learning using HE and TEE
Repo for Mphasis PPML Research Project
Health Score model implementation using Homomorphic Encryption to preserve data privacy.
Add a description, image, and links to the ppml topic page so that developers can more easily learn about it.
To associate your repository with the ppml topic, visit your repo's landing page and select "manage topics."