Welcome to ai-privacy-toolkit’s documentation!

This project provides tools for assessing and improving 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.

The dataset risk assessment module implements a tool for privacy assessment of synthetic datasets that are to be used in AI model training.

Indices and tables