Pinned Repositories
awesome-federated-learning
resources about federated learning and privacy in machine learning
crypto_nn
Proof of concept for CryptoDL made for BigSec course @ EURECOM
CryptoDL
Privacy-preserving Deep Learning based on homomorphic encryption (HE)
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.
federated_learning_with_encryption_by_paillier
It's an easy test.Finished!
Google-Machine-learning-crash-course
谷歌机器学习速成课程+机器学习术语表PDF+机器学习规则PDF 。该资源适用于机器学习、深度学习和TensorFlow爱好者参考！
graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
HEAAN
Hidden-Trigger-Backdoor-Attacks
Official Repository for the AAAI-20 paper "Hidden Trigger Backdoor Attacks"
SHE
WenTaoDu's Repositories
WenTaoDu/federated_learning_with_encryption_by_paillier
It's an easy test.Finished!
WenTaoDu/Google-Machine-learning-crash-course
谷歌机器学习速成课程+机器学习术语表PDF+机器学习规则PDF 。该资源适用于机器学习、深度学习和TensorFlow爱好者参考！
WenTaoDu/SHE
WenTaoDu/awesome-federated-learning
resources about federated learning and privacy in machine learning
WenTaoDu/crypto_nn
Proof of concept for CryptoDL made for BigSec course @ EURECOM
WenTaoDu/CryptoDL
Privacy-preserving Deep Learning based on homomorphic encryption (HE)
WenTaoDu/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.
WenTaoDu/graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
WenTaoDu/HEAAN
WenTaoDu/Hidden-Trigger-Backdoor-Attacks
Official Repository for the AAAI-20 paper "Hidden Trigger Backdoor Attacks"
WenTaoDu/IDASH2017
WenTaoDu/karateclub
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
WenTaoDu/leeml-notes
李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes
WenTaoDu/lihang-code
《统计学习方法》的代码实现
WenTaoDu/Machine-Learning-Session
WenTaoDu/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
WenTaoDu/ndk-samples
Android NDK samples with Android Studio
WenTaoDu/nlp-practice
WenTaoDu/pytorch_geometric
Geometric Deep Learning Extension Library for PyTorch
WenTaoDu/SEAL
Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library.
WenTaoDu/The-North-Remembers
WenTaoDu/vector-homomorphic-encryption
6.857 project - implementation of scheme for encrypting integer vectors that allows addition, linear transformation, and weighted inner products.