Popular GitHub Repositories for Homomorphic Encryptions

A unified framework for privacy-preserving data analysis and machine learning

IBM Fully Homomorphic Encryption Toolkit For Linux. This toolkit is a Linux based Docker container that demonstrates computing on encrypted data without decrypting it! The toolkit ships with two demos including a fully encrypted Machine Learning inference with a Neural Network and a Privacy-Preserving key-value search.

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

A Privacy-Preserving Framework Based on TensorFlow

PYthon For Homomorphic Encryption Libraries, perform encrypted computations such as sum, mult, scalar product or matrix multiplication in Python, with NumPy compatibility. Uses SEAL/PALISADE as backends, implemented using Cython.

Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic E

Privacy -preserving Neural Networks

Homomorphic Encryption and Federated Learning based Privacy-Preserving

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

Author's PyTorch Implementation of Deep Homomorphic Policy Gradient (DHPG) - NeurIPS 2022 and JMLR 2024

[arXiv'21] Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning

Implementation of a client reputation, gradient checking and homomorphic encryption mechanism to defend a federated learning system from data/model poisoning and reverse engineering attacks.

Proof of Concept of a Federated Learning framework that maintains the privacy of the participants involved.

Code for paper "Towards Privacy-Preserving, Real-Time and Lossless Feature Matching"

Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated

? This project implements the partially homomorphic Paillier algorithm and extends and optimizes encryption, decryption, matrix multiplication, and other operations on Numpy matrices, thereby enhancing speed.

A Homomorphic Encryption-Driven Python Framework for Secure Cloud-Based Facial Recognition