a9a93c8a3a
* QI updates * update code to support training ML on QI features * fix code so features that are not from QI should not be part of generalizations and add description * merging two branches, training on QI and on all data * adding tests and asserts |
||
|---|---|---|
| .. | ||
| __init__.py | ||
| anonymizer.py | ||
| README.md | ||
anonymization module
This 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 module contains methods that enable anonymizing training datasets in a manner that is tailored to and guided by an existing, trained ML model. It uses the existing model's predictions on the training data to train a second, anonymizer model, that eventually determines the generalizations that will be applied to the training data. For more information about the method see: https://arxiv.org/abs/2007.13086
Once the anonymized training data is returned, it can be used to retrain the model.
The following figure depicts the overall process:
