Toby Zhou's repositories
Secure-Face-Recognition-System-based-on-Fully-Homomorphic-Encryption
基于全同态加密的安全人脸识别系统/Secure Face Recognition System based on Fully Homomorphic Encryption
fruit-clustering-using-PCA
使用PCA(主成分分析)对四维特征值进行降维并且使用matplotlib进行画图显示聚类效果
fruit-detect-using-KNN
使用KNN算法训练水果属性模型并测试
fabric
Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.
CryptoNets
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 Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
HEBenchmark
Homomorphic encryption test
PySEAL
This repository is a fork of Microsoft Research's homomorphic encryption implementation, the Simple Encrypted Arithmetic Library (SEAL). This code wraps the SEAL build in a docker container and provides Python API's to the encryption library.
zju-icicles
浙江大学课程攻略共享计划