WeiBenqiang's repositories
Machine-Learning-From-Huang-Haiguang
学习黄海广博士的关于吴恩达的机器学习代码实现
2019Summer_SecureML
SecureML Introduction
chinese-copywriting-guidelines
Chinese copywriting guidelines for better written communication/中文文案排版指北
cmake-demo
《CMake入门实战》源码
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.
CryptoNets-with-Python
CryptoNets using Python and ctypes. This repository is part of the final project of Neural Networks at Sapienza University of Rome.
cuFHE
CUDA-accelerated Fully Homomorphic Encryption Library
FHElib
an implementation for TFHE
fpylll
A Python interface for https://github.com/fplll/fplll
gitskills
about gitskills
go-ethereum
Official Go implementation of the Ethereum protocol
HEBenchmark
Homomorphic encryption test
HElib
An Implementation of homomorphic encryption
how-to-write-makefile
跟我一起写Makefile重制版
keras
Deep Learning for humans
learngit
learn to use git
libsnark
C++ library for zkSNARKs
MachineLearning
learn ML
modern-resume-theme
A modern static resume template and theme. Powered by Jekyll and GitHub pages.
OTExtension
C++ OT extension implementation
privateml
Various material around private machine learning, some associated with blog
SEAL-Demo
Demos, Examples, Tutorials for using Microsoft SEAL library.
SecurityConferenceLectures
安全类会议演讲视频听写与翻译,仅限学习交流使用。
slambook2
edition 2 of the slambook
TinyGarble
TinyGarble: Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits
VSCode-and-CMake
learn how to use VSCode and CMake