IBM/differential-privacy-library
Diffprivlib: The IBM Differential Privacy Library
This project helps data scientists and researchers explore and experiment with differential privacy in machine learning and data analytics. You can feed in your standard datasets and get back models or data analyses that have privacy guarantees built-in. It's designed for anyone looking to understand or apply privacy-preserving techniques to their data-driven work.
906 stars. No commits in the last 6 months.
Use this if you need to build or evaluate machine learning models and data analyses while ensuring the privacy of individuals within your datasets, especially for research or educational purposes.
Not ideal if you are looking for a production-ready solution without engaging with the maintainers, as the public release is intended for research and education.
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906
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207
Language
Python
License
MIT
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Last pushed
Sep 17, 2025
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