hongshenghu Goto Github PK

ann-benchmarks

Benchmarks of approximate nearest neighbor libraries in Python

attacks-on-machine-learning

Survey of attacks on not just only Machine Learning, but Deep Learning

awesome-ml-privacy-attacks

backdoor_federated_learning

Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

blindmi

datasets

Processed datasets that we have used in our research

dba

DBA: Distributed Backdoor Attacks against Federated Learning

entangled-watermark

fedavgpy

On the Convergence of FedAvg on Non-IID Data

federated-learning

A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561

federated-learning-pytorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

fedgen

Code and data accompanying the FedGen paper

fedprox

Federated Optimization in Heterogeneous Networks (MLSys '20)

fraud-detect

Official implementation of Washing The Unwashable : On The (Im)possibility of Fairwashing Detection, NeurIPS 2022

gan-leaks

Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)

graph-adversarial-learning-literature

A curated list of adversarial attacks and defenses papers on graph-structured data.

hidden-trigger-backdoor-attacks

Official Repository for the AAAI-20 paper "Hidden Trigger Backdoor Attacks"

hongshenghu

hw1

label-consistent-backdoor-code

Code for "Label-Consistent Backdoor Attacks"

membership-inference

Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)

membership-inference-evaluation

Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models

membership-inference-machine-learning-literature

membership-inference-via-backdooring

membershipwhiteboxattacks

memguard

ml-privacy-regulization

ndss21-model-poisoning

Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"

privacy

Library for training machine learning models with privacy for training data

property-inference-collaborative-ml

Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)