Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
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Updated
May 31, 2024 - Python
Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
A Replication (and Tribute) of The Log of Gravity
A C++-based framework for privacy-preserving machine learning using HE and TEE
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.
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”.
Sisyphus: A Cautionary Tale of Using Polynomial Activations in Privacy-Preserving Deep Learning
A Learning Journal on (Privacy-Preserving) AI for Medicine and Healthcare
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
Repo for Mphasis PPML Research Project
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
Health Score model implementation using Homomorphic Encryption to preserve data privacy.
Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
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