A unified framework for privacy-preserving data analysis and machine learning
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
A library for Partially Homomorphic Encryption in Python
A Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.
A Lightweight Partially Homomorphic Encryption Library for Python
Privacy-Preserving Convolutional Neural Networks using Homomorphic Encryption
Privacy -preserving Neural Networks
Intel Paillier Cryptosystem Library is an open-source library which provides accelerated performance of a partial homomorphic encryption (HE), named Paillier cryptosystem, by utilizing Intel® IPP-Crypto technologies on Intel CPUs supporting the AVX512IFMA instructions. The library is written in modern standard C++ and provides the essential API …
Client/Server framework for encrypted machine learning using homomorphic encryption
Secure outsourcing of image editing to untrusted cloud environments by implementing Paillier's Homomorphic Cryptosystem and extending it to work over images. Implementing a homomorphic brightness function as proof of concept.
Federated Learning with Differential Privacy and Homomorphic Encryption.
Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
This repo contains code/learning resources related to Homomorphic Encryption.
TNO PET Lab - secure Multi-Party Computation (MPC) - Protocols - Distributed Keygen
Python implementation of "Somewhat Practical Fully Homomorphic Encryption"
[arXiv'21] Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning
💊 Homomorphic Encryption in Python
Open source platform for the privacy-preserving machine learning lifecycle
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