Table of Contents

• Security Policy for Machine Learning Systems
• Machine Learning Privacy-Preserving Techniques
• Tools for Securing Machine Learning
• Security Threats to Machine Learning
• ML Security Regulations and Standards

Security Policy for Machine Learning Systems

A ThalesGroup policy framework to secure machine learning datasets, models, underlying platform, compliance with internal and external regulations, and to humans involved.

Available at ML Security Policy with ML Security Requirements and ML Security Guidelines

Machine Learning Privacy-Preserving Techniques

Learn about cutting-edge privacy-preserving techniques for machine learning including Differential Privacy, Federated Learning, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Privacy-Preserving Data Synthesis in this comprehensive GitHub repository. Explore how these methods safeguard sensitive data while enabling collaborative analysis and model training.

Available at ML privacy-preserving techniques

Tools for Securing Machine Learning

Discover essential security tools for source code vulnerability detection, comprehensive attack and defense tools, ML supply chain security solutions, and privacy and compliance tools. Additionally, explore techniques for securing Jupyter notebooks, ensuring robust protection for your data, code, and models. Embrace a holistic approach to cybersecurity and data privacy in your development and analysis workflows.

Available at ML security tools

Security Threats to Machine Learning

Available at ML Security Threats

License

This project is licensed under the Creative Commons Attribution-NoDerivs 4.0 International (CC BY-ND 4.0) License. You can view the full license text here.

Project Contacts

For further information or to contribute to this project, you can reach out to the following contacts:

• Project Leader and Key contributor: Viswanath S Chirravuri
  LinkedIn

• Project Sponsors:

  • Gilles Durbec
    LinkedIn
  • Stephane Soustre
    LinkedIn