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Add AI privacy Dataset assessment module with two attack implementations. Signed-off-by: Maya Anderson <mayaa@il.ibm.com>
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45 lines
No EOL
2.4 KiB
Markdown
# ai-privacy-toolkit
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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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A toolkit for tools and techniques related to the privacy and compliance of AI models.
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The [**anonymization**](apt/anonymization/README.md) module contains methods for anonymizing ML model
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training data, so that when a model is retrained on the anonymized data, the model itself will also be
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considered 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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The [**minimization**](apt/minimization/README.md) module contains methods to help adhere to the data
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minimization principle in GDPR for ML models. It enables to reduce the amount of
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personal data needed to perform predictions with a machine learning model, while still enabling the model
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to make accurate predictions. This is done by by removing or generalizing some of the input features.
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Official ai-privacy-toolkit documentation: https://ai-privacy-toolkit.readthedocs.io/en/latest/
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Installation: pip install ai-privacy-toolkit
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For more information or help using or improving the toolkit, please contact Abigail Goldsteen at abigailt@il.ibm.com,
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or join our Slack channel: https://aip360.mybluemix.net/community.
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We welcome new contributors! If you're interested, take a look at our [**contribution guidelines**](https://github.com/IBM/ai-privacy-toolkit/wiki/Contributing).
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**Related toolkits:**
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ai-minimization-toolkit - has been migrated into this toolkit.
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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. Includes an attack module called *inference* that contains privacy attacks on ML models
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(membership inference, attribute inference, model inversion and database reconstruction) as well as a *privacy* metrics module that contains
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membership leakage metrics for ML models.
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Citation
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--------
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Abigail Goldsteen, Ola Saadi, Ron Shmelkin, Shlomit Shachor, Natalia Razinkov,
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"AI privacy toolkit", SoftwareX, Volume 22, 2023, 101352, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101352. |