- differential-privacy-library VS data-science-ipython-notebooks
- differential-privacy-library VS PyDP
- differential-privacy-library VS awesome-machine-unlearning
- differential-privacy-library VS fides
- differential-privacy-library VS PrivacyEngCollabSpace
- differential-privacy-library VS Keras
- differential-privacy-library VS transformers
- differential-privacy-library VS keras
- differential-privacy-library VS spaCy
Differential-privacy-library Alternatives
Similar projects and alternatives to differential-privacy-library
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data-science-ipython-notebooks
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PyDP
The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy
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differential-privacy-library reviews and mentions
Posts with mentions or reviews of differential-privacy-library.
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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.
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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.
Stats
Basic differential-privacy-library repo stats
2
776
4.8
23 days ago
IBM/differential-privacy-library is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of differential-privacy-library is Python.
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