2b2dab6bef
* Squashed commit of wrappers:
Wrapper minimizer
* apply dataset wrapper on minimizer
* apply changes on minimization notebook
* add black_box_access and unlimited_queries params
Dataset wrapper anonymizer
Add features_names to ArrayDataset
and allow providing features names in QI and Cat features not just indexes
update notebooks
categorical features and QI passed by indexes
dataset include feature names and is_pandas param
add pytorch Dataset
Remove redundant code.
Use data wrappers in model wrapper APIs.
add generic dataset components
Create initial version of wrappers for models
* Fix handling of categorical features
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| README.md | ||
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| setup.cfg | ||
ai-privacy-toolkit
A toolkit for tools and techniques related to the privacy and compliance of AI models.
The anonymization module contains methods for anonymizing ML model training data, so that when a model is retrained on the anonymized data, the model itself will also be considered anonymous. This may help exempt the model from different obligations and restrictions set out in data protection regulations such as GDPR, CCPA, etc.
The minimization module contains methods to help adhere to the data minimization principle in GDPR for ML models. It enables to reduce the amount of personal data needed to perform predictions with a machine learning model, while still enabling the model to make accurate predictions. This is done by by removing or generalizing some of the input features.
Official ai-privacy-toolkit documentation: https://ai-privacy-toolkit.readthedocs.io/en/latest/
Installation: pip install ai-privacy-toolkit
For more information or help using or improving the toolkit, please contact Abigail Goldsteen at abigailt@il.ibm.com, or join our Slack channel: https://aip360.mybluemix.net/community.
Related toolkits:
ai-minimization-toolkit - has been migrated into this toolkit.
differential-privacy-library: A general-purpose library for experimenting with, investigating and developing applications in, differential privacy.
adversarial-robustness-toolbox: A Python library for Machine Learning Security. Includes an attack module called inference that contains privacy attacks on ML models (membership inference, attribute inference, model inversion and database reconstruction) as well as a privacy metrics module that contains membership leakage metrics for ML models.