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CONTRIBUTING.md

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Contributing to the Secure Machine Learning Framework

Thank you for your interest in contributing to the Framework for Securing Machine Learning Projects. This project aims to provide comprehensive documentation on security activities, guidelines, and recommended security tools to secure the data, model, and underlying platform throughout the ML lifecycle.

How to Contribute

  1. Contact the Project Owner
    To contribute to this project, please reach out to Viswanath S Chirravuri to request access.
    When contacting, provide the following details:

    • Your corporate or personal email ID.
    • Your GitHub username/alias.
    • Consent to include your name as a contributor in future presentations or credits for the project.

  2. Clone the Repository
    After obtaining contributing access:

    • Clone the repository to your local machine: 
      git clone https://github.com/ThalesGroup/secure-ml.git
    • Open the project in your preferred IDE (e.g., VSCode).

  3. Making Changes

    • You can enhance the framework by adding new files or folders related to securing machine learning workflows, or by improving the existing documentation.
    • Follow these steps to contribute:
      1. Create a new branch for your contributions: 
        git checkout -b feature/your-feature-name
      2. Make the necessary changes or additions.
      3. Commit your changes with a clear message: 
        git commit -m "Add description of changes"
      4. Push your changes to GitHub: 
        git push origin feature/your-feature-name

  4. Submit a Pull Request
    Once your changes are pushed, submit a pull request (PR) for review. Include a detailed explanation of the changes made and why they are important.

  5. Code of Conduct
    All contributors must adhere to the project's Code of Conduct. Respectful and professional interactions are expected.

Contribution Scope

As this project focuses on securing machine learning systems, contributions should align with one or more of the following areas:

  • Documentation of security practices across the ML lifecycle.
  • Guidelines for securing ML data, models, and platforms.
  • Integration or recommendations for security tools.
  • Any additional security and privacy concerns for machine learning applications.

License

By contributing to this project, you agree that your contributions will be licensed under the same Creative Commons Attribution-NoDerivs 4.0 International License as the project.