fides
awesome-machine-unlearning
fides | awesome-machine-unlearning | |
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2 | 5 | |
328 | 609 | |
0.6% | - | |
9.8 | 7.9 | |
6 days ago | 23 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fides
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What data governance tool are you folks using?
I’ve also been impressed with the approach of Fides, an open source privacy management framework that ties into ci/cd, though I haven’t used it myself yet. The thing about it that stood out was Fideslang, their language and taxonomy for representing data privacy primitives.
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Privacy-as-Code: Preventing Facebook’s $5B violation using Fides Open-Source
Fides is built to solve for problems like this. In its current release, you can already draft a policy in YAML using fideslang and enforce that policy to ensure engineers across a team can’t accidentally or intentionally misuse data in a way that deviates from the promises a business or application makes to its users.
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?
fiftyone - The open-source tool for building high-quality datasets and computer vision models
differential-privacy-library - Diffprivlib: The IBM Differential Privacy Library
AIJack - Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
dvc - 🦉 ML Experiments and Data Management with Git
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
datahub - The Metadata Platform for your Data Stack
course-content-dl - NMA deep learning course
pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.
PyRedactKit - Python CLI tool to redact and un-redact sensitive data from text files. 🔐📝
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
fideslang - Open-source description language for privacy to declare data types and data behaviors in your tech stack in order to simplify data privacy globally. Supports GDPR, CCPA, LGPD and ISO 19944.