Economic effects of Chile FTAs and an eventual CTPP accession

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

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.

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

Sisyphus: A Cautionary Tale of Using Polynomial Activations in Privacy-Preserving Deep Learning

A compiled list of resources and materials for PPML

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.

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.

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

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”.

R package that provides estimation methods for Gravity Models

Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.