PyRedactKit
awesome-machine-unlearning
PyRedactKit | awesome-machine-unlearning | |
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3 | 5 | |
41 | 602 | |
- | - | |
1.7 | 7.9 | |
11 months ago | 11 days ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 only | MIT License |
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PyRedactKit
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Python based cli tool for redacting and un-redacting sensitive data
This sounds like a good idea I should explore. Could you point me to some possible reference projects I could look into for how the plugin system is implemented? Currently, I made the regex database extensible by making it an object.
- Open source data leak prevention tool
awesome-machine-unlearning
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[P] [R] Machine Unlearning Summary
Github Repo: https://github.com/tamlhp/awesome-machine-unlearning 📚 Notebook: https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten/
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[R] A Survey of Machine Unlearning
Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (ML), its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed from both computer systems and from ML models, i.e. ``the right to be forgotten''). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often `remember' the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this paper aspires to present a comprehensive examination of machine unlearning's concepts, scenarios, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope this survey serves as a valuable resource for ML researchers and those seeking to innovate privacy technologies. Our resources are publicly available at this https URL.
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Welcome!
Welcome to Machine unlearning, You can post all kinds of stuff about Machine unlearning here . Here is a great resource to get you started https://github.com/tamlhp/awesome-machine-unlearning
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[P] [R] [D] Can Machine Actually Forget Your Data?
We also have a Github repo for this topic, please consider star if this topic piques your curiosity.
- [P] Awesome Machine Unlearning
What are some alternatives?
MurMurHash - This little tool is to calculate a MurmurHash value of a favicon to hunt phishing websites on the Shodan platform.
differential-privacy-library - Diffprivlib: The IBM Differential Privacy Library
urlRecon - :pencil: urlRecon - Info Gathering or Recon tool for Urls -> Retrieves * Whois information of the domain * DNS Details of the domain * Server Fingerprint * IP geolocation of the server
AIJack - Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
fides - The Privacy Engineering & Compliance Framework
continual-pretraining-nlp-vision - Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
course-content-dl - NMA deep learning course