Pinned Repositories
fw-Stateful-firewall
网络安全课程设计——Linux下的状态检测防火墙
file_system_v2
bustub
The BusTub Relational Database Management System (Educational)
Cource-Designce-of-Software-Security
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.
database_lab
datafree-model-extraction
CVPR 2021 Official repository for the Data-Free Model Extraction paper. https://arxiv.org/abs/2011.14779
delphi
A Cryptographic Inference Service for Neural Networks
Device_Driver
OS course design
FedGen
Code and data accompanying the FedGen paper
Leslie-ClClCl's Repositories
Leslie-ClClCl/bustub
The BusTub Relational Database Management System (Educational)
Leslie-ClClCl/datafree-model-extraction
CVPR 2021 Official repository for the Data-Free Model Extraction paper. https://arxiv.org/abs/2011.14779
Leslie-ClClCl/FedML
A Research-oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation. Best Paper Award at NeurIPS 2020 Federated Learning workshop. Join our Slack Community:(https://join.slack.com/t/fedml/shared_invite/zt-havwx1ee-a1xfOUrATNfc9DFqU~r34w)
Leslie-ClClCl/honest-but-curious-nets
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs (ACM CCS'21)
Leslie-ClClCl/FedGen
Code and data accompanying the FedGen paper
Leslie-ClClCl/fedmd_torch
Leslie-ClClCl/FedMD_clean
FedMD: Heterogenous Federated Learning via Model Distillation
Leslie-ClClCl/delphi
A Cryptographic Inference Service for Neural Networks
Leslie-ClClCl/HElib
HElib is an open-source software library that implements homomorphic encryption. It supports the BGV scheme with bootstrapping and the Approximate Number CKKS scheme. HElib also includes optimizations for efficient homomorphic evaluation, focusing on effective use of ciphertext packing techniques and on the Gentry-Halevi-Smart optimizations.
Leslie-ClClCl/fw-Stateful-firewall
网络安全课程设计——Linux下的状态检测防火墙
Leslie-ClClCl/system_call
OS course design
Leslie-ClClCl/Device_Driver
OS course design
Leslie-ClClCl/Monitor
OS Course Design
Leslie-ClClCl/file_system_v2
Leslie-ClClCl/database_lab
Leslie-ClClCl/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.
Leslie-ClClCl/Cource-Designce-of-Software-Security
Leslie-ClClCl/gazelle_mpc