2021-04-28 06:25:00 -04:00
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# ai-privacy-toolkit
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2021-04-28 14:00:19 +03:00
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<p align="center">
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<img src="docs/images/logo with text.jpg?raw=true" width="467" title="ai-privacy-toolkit logo">
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</p>
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<br />
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2021-04-28 06:25:00 -04:00
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A toolkit for tools and techniques related to the privacy and compliance of AI models.
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2021-04-28 14:00:19 +03:00
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The first release of this toolkit contains a single module called [**anonymization**](apt/anonymization/README.md).
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This module contains methods for anonymizing ML model training data, so that when
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a model is retrained on the anonymized data, the model itself will also be considered
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anonymous. This may help exempt the model from different obligations and restrictions
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set out in data protection regulations such as GDPR, CCPA, etc.
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Official ai-privacy-toolkit documentation: <add link to readthedocs>
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**Related toolkits:**
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[ai-minimization-toolkit](https://github.com/IBM/ai-minimization-toolkit): A toolkit for
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reducing the amount of personal data needed to perform predictions with a machine learning model
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[differential-privacy-library](https://github.com/IBM/differential-privacy-library): A
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general-purpose library for experimenting with, investigating and developing applications in,
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differential privacy.
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[adversarial-robustness-toolbox](https://github.com/Trusted-AI/adversarial-robustness-toolbox):
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A Python library for Machine Learning Security.
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