AmitHasanShuvo
PhD Student in School of Computing at Queen's University | Ex-ML Engineer at ACI Limited | Kaggle Competition Expert (x4)
Queen's UniversityCanada
AmitHasanShuvo's Stars
HarisIqbal88/PlotNeuralNet
Latex code for making neural networks diagrams
eugeneyan/open-llms
📋 A list of open LLMs available for commercial use.
PyGithub/PyGithub
Typed interactions with the GitHub API v3
CVEProject/cvelist
Pilot program for CVE submission through GitHub. CVE Record Submission via Pilot PRs ending 6/30/2023
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
swimlane/pyattck
A Python package to interact with the Mitre ATT&CK Framework
center-for-threat-informed-defense/attack_to_cve
🚨ATTENTION🚨 The CVE mappings have migrated to the Center’s Mappings Explorer project. See README below. This repository is kept here as an archive.
ALFA-group/BRON
"Linking Threat Tactics, Techniques, and Patterns with Defensive Weaknesses, Vulnerabilities and Affected Platform Configurations for Cyber Hunting" by Erik Hemberg, Jonathan Kelly, Michal Shlapentokh-Rothman, Bryn Reinstadler, Katherine Xu, Nick Rutar, Una-May O'Reilly
AI-Tech-Research-Lab/PyCrCNN
Privacy-Preserving Convolutional Neural Networks using Homomorphic Encryption
jmartin82/mkpis
Measuring the development process for Gitflow managed projects.
gdalle/phd-resources
Not the research toolbox you deserve, but the one you need right now.
ltzheng/data-privacy
Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, etc.
neilernst/cliffsDelta
mehdigolzadeh/BoDeGHa
A python tool to predict the identity type in github activities (Human,Bot)
satssehgal/Homomorphic-Encryption
FebriantiW/Homomorphic-Encryption-and-Federated-Learning-based-Privacy-Preserving-CNN-Training-
Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learn- ing techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this prob- lem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomor- phic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.
readerbench/CVE2ATT-CK
CVE2ATT&CK: BERT-based mapping of CVEs to MITRE ATT&CK Techniques
simonZhou86/EvolutionaryComputing
Genetic Algorithm based optimization for CNN parameters
souravs17031999/Federatedencryption-showcase
The project showcasing federated learning of model and testing on encrypted data and model
IlyasAzeem/tutorial
This repository contains source code of the tutorial on PyGithub library.
yangzhangs/cd_replication
Our reproducibility package (data and scripts) for CD study. Previous name is: Anonymous-cd/cd_replication
azezezaaa/MIAR_M2
This repository contains the code for the M2 thesis on Homomorphic Encryption, Federated Learning and Secure Aggregation. Implemented Homomorphic Encryption schemes are CKKS and Gentry's.
dhruv-singhal-github/Secure-Machine-Learning
This a project that secures the data being used for machine learning as a service using various approaches -federated machine learning and homomorphic Encryption
YutaMiyake/morphing
Face morphing on jupyter notebook
iahsanujunda/privacy-ai
Secure and private AI with differential privacy, federated learning, and encryption in machine learning
NAIST-SE/Vulnerability-Fix-Lags-Release-Adoption-Propagation
Replication Artifact for EMSE Submission
AmitHasanShuvo/PR-stats
PR-stats is an open-source python library that brings different functions to bring stats about pull requests.
RISElabQueens/PR-Accelerator
PR-Accelerator is a set of tools that reports analytics and information regarding pull requests (PRs) and points out the delays in first response. This tool was presented in our paper titled "Understanding the Time to First Response In GitHub Pull Requests" published at the MSR 2023 conference.
atrautsch/icsme2020_replication
Replication kit for Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction
IlyasAzeem/PRs_project