Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
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Updated
Feb 8, 2020 - Python
Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
Health Score model implementation using Homomorphic Encryption to preserve data privacy.
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
Repo for Mphasis PPML Research Project
A compiled list of resources and materials for PPML
Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022
A Learning Journal on (Privacy-Preserving) AI for Medicine and Healthcare
Sisyphus: A Cautionary Tale of Using Polynomial Activations in Privacy-Preserving Deep 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”.
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.
A C++-based framework for privacy-preserving machine learning using HE and TEE
A Replication (and Tribute) of The Log of Gravity
Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
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