Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
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Updated
Aug 31, 2024 - Python
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
A compiled list of resources and materials for PPML
Sisyphus: A Cautionary Tale of Using Polynomial Activations in Privacy-Preserving Deep Learning
Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022
Repo for Mphasis PPML Research Project
A Learning Journal on (Privacy-Preserving) AI for Medicine and Healthcare
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”.
Economic effects of Chile FTAs and an eventual CTPP accession
A C++-based framework for privacy-preserving machine learning using HE and TEE
A Replication (and Tribute) of The Log of Gravity
A capstone project in collaboration with Zama to develop a privacy-preserving machine learning model using PPML, FHE, and Concrete ML for predicting CV relevance to job offers.
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