securedf 0.1.1
Protect sensitive data with securedf, a Python package offering encryption, anonymization, and compliance tools for data scientists.
License: MIT

securedf: Secure Data Privacy Framework for Python Data Scientists
======================================================================

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securedf is an all-in-one Python package designed to address data privacy and security concerns for data scientists. Developed by `Deependra Verma <https://www.linkedin.com/in/deependra-verma-data-science/>`__, secure-learn offers robust encryption, anonymization, and access control tools, ensuring the confidentiality and integrity of sensitive data.

Introduction
------------

securedf: Your all-in-one Python package for robust data privacy and security. Encrypt, anonymize, and control access to sensitive data effortlessly.

Features
--------

securedf provides the following key methods:

- ``encrypt_data(data)``: Encrypts sensitive data to ensure confidentiality.
- ``decrypt_data(encrypted_data)``: Decrypts encrypted data to its original form.
- ``anonymize_data(data, columns_to_anonymize)``: Anonymizes specific columns in a DataFrame.
- ``add_role(role_name, permissions)``: Adds a new role with associated permissions to the access control system.
- ``check_permission(role_name, permission)``: Checks if a role has the specified permission.

Installation
------------

To install securedf, simply run:

.. code-block:: bash

    pip install securedf

Alternatively, you can clone the GitHub repository:

.. code-block:: bash

    git clone https://github.com/DeependraVerma/SecuPy-Secure-Data-Privacy-Framework-for-Python-Data-Scientists.git
    cd SecuPy-Secure-Data-Privacy-Framework-for-Python-Data-Scientists
    python setup.py install

Dependencies
------------

securedf relies on the following dependencies:

- ``pandas>=1.0.0``
- ``faker>=8.0.0``
- ``cryptography>=3.0``

Usage
-----

Import the package:

.. code-block:: python

    from PrivacyPy import DataPrivacyFramework

Initialize PrivacyPy with encryption key:

.. code-block:: python

    encryption_key = "your_encryption_key"
    privacy_framework = DataPrivacyFramework(encryption_key)

Anonymize sensitive columns (Name, Email):

.. code-block:: python

    anonymized_df = privacy_framework.anonymize_data(data, ['Name', 'Email'])

Encrypt entire DataFrame:

.. code-block:: python

    encrypted_df = privacy_framework.encrypt_data(anonymized_df)
    print("Encrypted DataFrame:")
    print(encrypted_df)

Users Benefit
-------------

securedf empowers data scientists with the following benefits:

- **Data Confidentiality:** Encrypt sensitive data to prevent unauthorized access.
- **Anonymization:** Anonymize personally identifiable information for privacy protection.
- **Access Control:** Control data access based on user roles and permissions.
- **Compliance:** Ensure compliance with data protection regulations (e.g., GDPR, HIPAA).

Use Cases
---------

securedf can be used in various data science scenarios, including:

- Healthcare data analysis
- Financial data processing
- User authentication systems
- Research collaborations with external parties

Invitation for Contribution
---------------------------

Contributions to securedf are welcome! To contribute, follow these steps:

1. Fork the repository on GitHub.
2. Clone the forked repository to your local machine.
3. Create a new branch for your changes.
4. Make your modifications and improvements.
5. Test your changes to ensure they work as expected.
6. Commit your changes and push them to your forked repository.
7. Submit a pull request to the original repository.

We welcome contributions from the community! Whether it's fixing bugs, adding new features, or improving documentation, your contributions help make XplainML better for everyone. Check out our `Contributing Guidelines <https://github.com/DeependraVerma/SecuPy-Secure-Data-Privacy-Framework-for-Python-Data-Scientists/wiki>`__ to get started.

License
-------

securedf is licensed under the `MIT License <https://github.com/DeependraVerma/SecuPy-Secure-Data-Privacy-Framework-for-Python-Data-Scientists/blob/main/LICENSE>`__. See the `LICENSE <https://github.com/DeependraVerma/SecuPy-Secure-Data-Privacy-Framework-for-Python-Data-Scientists/blob/main/LICENSE>`__ file for details.

About the Author
----------------

`Email <mailto:deependra.verma00@gmail.com>`__ | `LinkedIn <https://www.linkedin.com/in/deependra-verma-data-science/>`__ | `GitHub <https://github.com/DeependraVerma>`__ | `Portfolio <https://deependradatascience-productportfolio.netlify.app/>`__