Added EdgeLock-X: Trusted Execution and In-Memory ML Model Protection Framework for Face Authentication

[J-B-Mugundh]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

---

Issue Title

Please enter the title of the issue related to your pull request.
Enter the issue title here. - EdgeLock-X: Trusted Execution and In-Memory ML Model Protection Framework for Face Authentication

• I have provided the issue title.

---

Info about the Related Issue

What's the goal of the project?
Describe the aim of the project. - To develop a Trusted Execution Environment (TEE) and an In-Memory Machine Learning (ML) Model Protection Framework utilizing advanced encryption and obfuscation techniques for secure face authentication. This framework addresses the critical challenge faced by UIDAI in safely loading ML models into a browser context.

• I have described the aim of the project.

---

Name

Please mention your name.
Enter your name here. - J B Mugundh

• I have provided my name.

---

GitHub ID

Please mention your GitHub ID.
Enter your GitHub ID here. - https://github.com/J-B-Mugundh

• I have provided my GitHub ID.

---

Email ID

Please mention your email ID for further communication.
Enter your email ID here. - mugundhjb@gmail.com

• I have provided my email ID.

---

Identify Yourself

Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).
Enter your participant role here. - GSSOC-EXTD

• I have mentioned my participant role.

---

Closes

Enter the issue number that will be closed through this PR.
Closes: #issue-number - #293

• I have provided the issue number.

---

Describe the Add-ons or Changes You've Made

Give a clear description of what you have added or modified.
Describe your changes here. - Added EdgeLock-X, which is designed to enhance the security of face authentication systems by creating a robust environment that protects sensitive ML models during execution. By implementing a Trusted Execution Environment, the framework ensures that ML models operate within a secure enclave, safeguarding them against unauthorized access and tampering. Additionally, the use of in-memory encryption and obfuscation techniques provides an additional layer of protection, preventing exposure of decrypted models even in volatile memory. This solution not only aims to improve the security of face authentication processes but also to ensure compliance with privacy regulations and standards. Ultimately, EdgeLock-X represents a significant step forward in securing facial recognition technologies in browser environments, addressing the urgent needs of UIDAI and similar organizations.

• I have described my changes.

---

Type of Change

Select the type of change:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

---

How Has This Been Tested?

Describe how your changes have been tested.
Describe your testing process here. - Manual Testing in local

• I have described my testing process.

---

Checklist

Please confirm the following:

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

👋 Thank you for opening this pull request! We're excited to review your contribution. Please give us a moment, and we'll get back to you shortly!

Feel free to join our community on Discord to discuss more!

[J-B-Mugundh]

@UTSAVS26 Could u please merge this if everything is fine?