Popular GitHub Repositories for Homomorphic Encryption

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.

Privacy -preserving Neural Networks

Homomorphic Encryption and Federated Learning based Privacy-Preserving

Federated Learning with Differential Privacy and Homomorphic Encryption.

Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning

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

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

Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated

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.

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

[CVPR 2023] CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss

Official implementation for paper "FedBoosting: Federated Learning with Gradient Protected Boosting for Text Recognition"

FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.

An adaptive batch homomorphic encryption framework for cross-device Federated Learning, which determines cost-efficient and sufficiently secure encryption strategies for clients with heterogeneous data and system capabilities.

Fully Homomorphic Encryption for Private Federated Learning

This repository contains the implementation of the paper "FedCrypt: A Dynamic White-Box Watermarking Scheme for Homomorphic Federated Learning". The paper is under review to the IEEE Transactions on Dependable and Secure Computing journal.

FedAnil++ is a Privacy-Preserving and Communication-Efficient Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil++ written in Python.

FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.

Flexible federated learning enables institutions to jointly train deep learning models even when data is non-uniformly labeled. The resulting models are superior to models which are trained with conventional methods.