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This is the official repository of the Quantumacy project, for more information check our <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy/blob/main">website</a>.<h3>Content</h3><ul><li>QKDSimkit is a simulator for quantum key distribution, it allows to exchange symmetric keys, there are two different interfaces: peer-to-peer and client-server; <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy/blob/main/QKDSimkit">QKDSimkit</a></li><li>Fork of Openfl that integrates QKDSimkit in its transport protocol; <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy/blob/main/openfl-develop">openfl-develop</a></li><li>Some of the deep learning models we used in our use cases, for example, chestscan is used in openfl-develop; <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy/blob/main/dl_models">dl_models</a></li><li>The Homomorphic Encryption Inference Use Case proposes an innovative setup where a two-fold quantum resilient encryption setup (QKD + Homomorphic Encryption) is used for privacy-preserving machine learning inference. It is based on 3 entities, client, storage, and server (processing). <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy/blob/main/homomorphic-encryption-use-case">homomorphic-encryption-use-case</a></li></ul><a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/CERN/Quantumacy">https://github.com/CERN/Quantumacy</a>

Quantumacy

Liberate.FHE is an open-source Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.Liberate.FHE is designed to be user-friendly while delivering robust performance, high accuracy, and a comprehensive suite of convenient APIs for developing real-world privacy-preserving applications.Liberate.FHE is a pure Python and CUDA implementation of FHE. So, Liberate.FHE supports multi-GPU operations natively.The main idea behind the design decisions is that non-cryptographers can use the library; it should be easily hackable and integrated with more extensive software frameworks.Additionally, several design decisions were made to maximize the usability of the developed software:<ul><li>Make the number of dependencies minimal.</li><li>Make the software easily hackable.</li><li>Set the usage of multiple GPUs as the default.</li><li>Make the resulting library easily integrated with the pre-existing software, especially Artificial Intelligence (AI) related ones.</li></ul><a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/desilo/liberate-fhe">https://github.com/desilo/liberate-fhe</a>

Liberate.FHE

수요예측 시스템의 구축 및 데이터 분석

QKD 시뮬레이터Homomorphic Encryption 시스템 구축Quatum Computing을 활용한 GAN

Homomorphic Encryiton Library 연구 및 개발