lcmfq's Stars
SOFAEnclave/SOFAEnclave
With a goal to make confidential computing easier, SOFAEnclave is a software stack consisting of key technologies such as HW/SW enclave implementations, enclave operating systems, and enclave orchestration and services.
tregua87/sgxmonitor-artifact
SgxMonitor Artifact for ACSAC '22
rcore-os/rcore-fs
The file system module for rCore OS.
FiloSottile/mkcert
A simple zero-config tool to make locally trusted development certificates with any names you'd like.
asterinas/mlsdisk
Multilayered, Log-structured Secure Disk (MlsDisk) protects the disk I/O for TEEs
lcmfq/hyperenclave
sebva/stress-sgx
Stress-SGX is a fork of stress-ng that allows to put Intel SGX enclaves under high load.
utds3lab/sgx-nbench
The nbench benchmark ported to SGX.
HyperEnclave/atc22-ae
xiaohuiduan/pbft
pbft算法基于Socket的java实现
0xPolygonZero/bft-simulation
Simulation of several BFT consensus algorithms for benchmarking
fangvv/SPBFT
《一种面向区块链的优化PBFT共识算法》基础源代码
caojohnny/pbft-java
A Practical Byzantine Fault Tolerance (PBFT) emulator built in Java
jianwei20/FastBFT_ethereum
lcmfq/minbft
Implementation of MinBFT consensus protocol.
bearDream/minbft
Implementation of MinBFT consensus protocol.
OpenviewtechCom/FISCO-BCOS-ON-K8S
在k8s上部署fisco-bcos
ietf-rats-wg/architecture
RATS Architecture
apache/incubator-teaclave-sgx-sdk
Apache Teaclave (incubating) SGX SDK helps developers to write Intel SGX applications in the Rust programming language, and also known as Rust SGX SDK.
google/private-join-and-compute
pytorch/opacus
Training PyTorch models with differential privacy
opendp/smartnoise-core-python
Python language bindings for smartnoise-core.
opendp/opendp
The core library of differential privacy algorithms powering the OpenDP Project.
tensorflow/privacy
Library for training machine learning models with privacy for training data
FederatedAI/Practicing-Federated-Learning
zcash/libsnark
libsnark: a C++ library for zkSNARK proofs
WaverleyLabs/SDPcontroller
Control Module for Software Defined Perimeter (SDP)
ehousecy/htlc-samples
Ebaas hash time locked cross chain samples.
jcpeterson/omi
The One Million Impressions (OMI) face dataset from Peterson et al. (PNAS, 2022)
microsoft/CCF
Confidential Consortium Framework