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

R package that provides estimation methods for Gravity Models

Samples of multi-class text classification with Differential Privacy Tensorflow 2.0

A compiled list of resources and materials for PPML

Curl: Private LLMs through Wavelet-Encoded Look-Up Tables

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

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 capstone project in collaboration with Zama to develop a privacy-preserving machine learning model using PPML, FHE and Concrete ML to detect banking frauds.

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.