Kaiyan Chang's repositories
TPIM-simulator
TPIM-GUI-Simulator
vscode
Visual Studio Code
PythonPlantsVsZombies
a simple PlantsVsZombies game
tensorlayer
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥
Principles-and-Practices-of-Interconnection-Networks--Chinese
《互联网络原理与实践（中文翻译）》Principles and Practices of Interconnection Networks
Zynq-Design-using-Vivado
This XUP course provides an introduction to embedded system design on Zynq using the Xilinx Vivado software suite.
hugo-academic
📝 The website builder for Hugo. Build and deploy a beautiful website in minutes!
researchapp
科研管理平台，帮助提高科研效率。
hugo-theme-m10c
A minimalistic (m10c) blog theme for Hugo
django-suit
Modern theme for Django admin interface
simpleui_demo
django simpleui demo.
InterpretableMLBook
《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版
tensorflow2snippets
Tensorflow 2.0 Snippets for VS Code (Go to VS Code Market and find Tensorflow 2.0 Snippets)
incubator-tvm
Open deep learning compiler stack for cpu, gpu and specialized accelerators
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.