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

:red_circle: Title : EdgeLock-X: Trusted Execution and In-Memory ML Model Protection Framework for Face Authentication :red_circle: Aim : 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. :red_circle: Brief Explanation : EdgeLock-X 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.

Screenshots 📷

:white_check_mark: To be Mentioned while taking the issue :

Full name : J B Mugundh
What is your participant role? GSSOC-EXTD

Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

UTSAVS26 / PyVerse

📃: EdgeLock-X: Trusted Execution and In-Memory ML Model Protection Framework for Face Authentication #293

Screenshots 📷