fides
differential-privacy-library
fides | differential-privacy-library | |
---|---|---|
2 | 2 | |
328 | 779 | |
0.6% | 1.2% | |
9.8 | 4.8 | |
6 days ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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
-
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.
-
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.
differential-privacy-library
-
Well, crackers.
Differential privacy. Basically i wanted to create a randomly generated database file, akin to medical records, create a Private Aggregation of Teacher Ensembles algorithms based on 20-60% of its content and then use this teacher model on the other 80-40% of database which was just a plaintext, not that that matters. The problem is, I've barely got ideas on how it all works, and the one example I've found used Cryptonumeric's library called cn.protect. And that went like I've already described. I've fallen back on practical part of the paper and found another way of getting any practical usage as the assignment requires and now am trying to use https://github.com/IBM/differential-privacy-library and the example on 30s guide to instead make the practical part about choosing epsilon ( a measure of how much information you can give away as a result of one query on the database to a third malicious party) by tracking associated accuracy of result dataset compared to original. I hope I'll manage to edit the code to accept my text file after parsing it through into ndarray from txt, separating the last column to use as a target and going from there.
-
Differential Privacy project on Python
IBM's Diffprivlib is a well-documented implementation of differential privacy in Python. Source code and getting started documentation is available on the IBM differential-privacy-library Github repository.
What are some alternatives?
fiftyone - The open-source tool for building high-quality datasets and computer vision models
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
dvc - 🦉 ML Experiments and Data Management with Git
PyDP - The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
datahub - The Metadata Platform for your Data Stack
awesome-machine-unlearning - Awesome Machine Unlearning (A Survey of Machine Unlearning)
PrivacyEngCollabSpace - Privacy Engineering Collaboration Space
pandas-datareader - Extract data from a wide range of Internet sources into a pandas DataFrame.
Keras - Deep Learning for humans
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.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.