Experiments to measure the energy consumption of various privacy-enhancing technologies

A Replication (and Tribute) of The Log of Gravity

Economic effects of Chile FTAs and an eventual CTPP accession

End-to-End Python econometric pipeline for modeling geopolitical risk in international trade using advanced Bayesian filtering, high-dimensional fixed effects, and split-panel jackknife inference. Replicates Hardwick's (2025) methodology with full robustness testing and automated reporting.

A C++-based framework for privacy-preserving machine learning using HE and TEE

🌍 Model political distance and trade dynamics using a quantitative framework to enhance understanding of international trade relationships.

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.

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”.

Repo for Mphasis PPML Research Project

Hands-on part of the Federated Learning and Privacy-Preserving ML tutorial given at VISUM 2022

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.

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

Explore detailed technical guides for the Zama FHE protocol. Set up your environment on Linux, Windows, or GitHub Codespace. 🚀💻

A compiled list of resources and materials for PPML

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

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