IBM/ai-privacy-toolkit

A toolkit for tools and techniques related to the privacy and compliance of AI models.

58
/ 100
Established

This toolkit helps organizations ensure their AI models comply with data protection regulations like GDPR and CCPA. It takes your machine learning model's training data or the model itself and applies anonymization and minimization techniques. The output is a modified dataset or model that requires less personal information or is considered anonymous, helping privacy officers and data scientists reduce regulatory risk.

110 stars. No commits in the last 6 months. Available on PyPI.

Use this if you need to build or deploy AI models while adhering to strict data privacy principles and regulations, reducing the risk of non-compliance.

Not ideal if you are looking for general-purpose machine learning security against adversarial attacks or a library solely focused on differential privacy.

data-privacy GDPR-compliance AI-ethics data-governance machine-learning-operations
Stale 6m No Dependents
Maintenance 2 / 25
Adoption 9 / 25
Maturity 25 / 25
Community 22 / 25

How are scores calculated?

Stars

110

Forks

47

Language

Python

License

MIT

Last pushed

Sep 17, 2025

Commits (30d)

0

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