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Welcome to SecureML Documentation
================================

.. image:: _static/secureml_logo.png
   :width: 300px
   :alt: SecureML Logo
   :align: center

**Privacy-Preserving Machine Learning Made Simple**

SecureML is a Python library that enables machine learning engineers to build and deploy models while maintaining compliance with privacy regulations like GDPR, CCPA, and HIPAA. 

The library provides tools for:

* **Data Anonymization**: K-anonymity, pseudonymization, and data masking
* **Privacy-Preserving Training**: Differential privacy and federated learning
* **Synthetic Data Generation**: Create realistic but private datasets
* **Compliance Checking**: Automated compliance verification
* **Audit Trails**: Comprehensive logging for documentation

.. toctree::
   :maxdepth: 2
   :caption: Contents:

   installation
   quickstart
   user_guide/index
   examples/index
   api/index
   contributing
   changelog

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`