Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
PhantomFHE: A CUDA-Accelerated Homomorphic Encryption Library
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
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Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
A compiled list of resources and materials for PPML
Samples of multi-class text classification with Differential Privacy Tensorflow 2.0
Explore detailed technical guides for the Zama FHE protocol. Set up your environment on Linux, Windows, or GitHub Codespace. 🚀💻
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
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.
Repo for Mphasis PPML Research Project
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”.
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.
Economic effects of Chile FTAs and an eventual CTPP accession
🌍 Model political distance and trade dynamics using a quantitative framework to enhance understanding of international trade relationships.
A C++-based framework for privacy-preserving machine learning using HE and TEE
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.
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