mirror of
https://github.com/IBM/ai-privacy-toolkit.git
synced 2026-04-27 13:56:22 +02:00
8a9ef80146
* Remove tensorflow dependency if not using keras model * Remove xgboost dependency if not using xgboost model * Documentation updates Signed-off-by: abigailt <abigailt@il.ibm.com>
43 lines
1.5 KiB
ReStructuredText
43 lines
1.5 KiB
ReStructuredText
.. ai-privacy-toolkit documentation master file, created by
|
|
sphinx-quickstart on Mon Feb 15 12:42:20 2021.
|
|
You can adapt this file completely to your liking, but it should at least
|
|
contain the root `toctree` directive.
|
|
|
|
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.
|
|
|
|
.. toctree::
|
|
:maxdepth: 2
|
|
:caption: Getting Started:
|
|
|
|
source/quick_start
|
|
|
|
.. toctree::
|
|
:maxdepth: 2
|
|
:hidden:
|
|
:caption: API
|
|
|
|
source/apt
|
|
|
|
|
|
|
|
Indices and tables
|
|
==================
|
|
|
|
* :ref:`genindex`
|
|
* :ref:`modindex`
|
|
* :ref:`search`
|