Awesome Ml Privacy Attacks
An awesome list of papers on privacy attacks against machine learning
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Adversarial Robustness Toolbox | 4,420 |  | 9 | 18 days ago | 56 | September 22, 2023 | 145 | mit | Python | |
| Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams | ||||||||||
| Pfllib | 1,147 | 3 days ago | 7 | gpl-2.0 | Python | |||||
| We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study | ||||||||||
| Ml_privacy_meter | 501 | 5 months ago | 1 | May 13, 2022 | 12 | mit | Jupyter Notebook | |||
| Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms. | ||||||||||
| Awesome Ml Privacy Attacks | 488 | 3 months ago | ||||||||
| An awesome list of papers on privacy attacks against machine learning | ||||||||||
| Deep Spying | 173 | 7 years ago | apache-2.0 | Python | ||||||
| Spying using Smartwatch and Deep Learning | ||||||||||
| Privacyraven | 172 | 10 months ago | 36 | apache-2.0 | Python | |||||
| Privacy Testing for Deep Learning | ||||||||||
| Robustdg | 160 | 10 months ago | 13 | mit | Python | |||||
| Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks. | ||||||||||
| Evaluatingdpml | 112 | a year ago | 1 | mit | Python | |||||
| This project's goal is to evaluate the privacy leakage of differentially private machine learning models. | ||||||||||
| Mia | 81 | 3 years ago | 4 | September 23, 2018 | 15 | mit | Python | |||
| A library for running membership inference attacks against ML models | ||||||||||
| Privpkt | 81 | a year ago | 26 | mit | Python | |||||
| Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification) | ||||||||||
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