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.

We welcome new contributors! If you're interested, take a look at our contribution guidelines.

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.