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SecureML Introduction
ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Chinese copywriting guidelines for better written communication/中文文案排版指北
《CMake入门实战》源码
Proof of concept for CryptoDL made for BigSec course @ EURECOM
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 using Python and ctypes. This repository is part of the final project of Neural Networks at Sapienza University of Rome.
CUDA-accelerated Fully Homomorphic Encryption Library
Implementation of the DGHV fully homomorphic encryption scheme
Implementation of Brakerski's leveled homomorphic encryption system
an implementation for TFHE
A Python interface for https://github.com/fplll/fplll
Faster homomorphic evaluation of SM4 and AES based on TFHE
about gitskills
Official Go implementation of the Ethereum protocol
Homomorphic encryption test
An Implementation of homomorphic encryption
repo contains some refrence material about homomorphic encryption
Proof-of-concept implementation of a homomorphic Simon encryption using YASHE and FV leveled homomorphic cryptosystems
跟我一起写Makefile重制版
Deep Learning for humans
learn to use git
C++ library for zkSNARKs
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.