stratosphereips / awesome-ml-privacy-attacks
An awesome list of papers on privacy attacks against machine learning
☆552Updated 6 months ago
Related projects: ⓘ
- Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.☆581Updated 3 weeks ago
- ☆269Updated 3 months ago
- Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.☆331Updated last year
- A library for running membership inference attacks against ML models☆137Updated last year
- Breaching privacy in federated learning scenarios for vision and text☆260Updated 5 months ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆117Updated 5 months ago
- TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classifica…☆274Updated last month
- autodp: A flexible and easy-to-use package for differential privacy☆260Updated 9 months ago
- Algorithms to recover input data from their gradient signal through a neural network☆260Updated last year
- A curated list of Meachine learning Security & Privacy papers published in security top-4 conferences (IEEE S&P, ACM CCS, USENIX Security…☆200Updated last month
- A curated list of resources for model inversion attack (MIA).☆115Updated 2 months ago
- Differentially Private Optimization for PyTorch 👁🙅♀️☆183Updated 4 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆176Updated 6 years ago
- A curated list of academic events on AI Security & Privacy☆128Updated 3 weeks ago
- Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)☆354Updated 4 months ago
- ☆133Updated 4 months ago
- Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and P…☆266Updated 4 years ago
- ☆159Updated 11 months ago
- [NeurIPS 2019] Deep Leakage From Gradients☆398Updated 2 years ago
- Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)☆271Updated last month
- Differential private machine learning☆177Updated 2 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- Privacy Testing for Deep Learning☆183Updated last year
- list of differential-privacy related resources☆279Updated 5 months ago
- Code for ML Doctor☆84Updated last month
- A curated list of trustworthy deep learning papers. Daily updating...☆336Updated last week
- A codebase that makes differentially private training of transformers easy.☆151Updated last year
- Implementation of dp-based federated learning framework using PyTorch☆277Updated last year
- ☆283Updated 2 months ago
- The code for "Improved Deep Leakage from Gradients" (iDLG).☆139Updated 3 years ago