Watch a 13-minute conference presentation from USENIX Security '24 exploring FAMOS, an innovative authentication framework for mobile payment applications. Learn how this system leverages federated multi-modal contrastive learning to enhance user authentication security while protecting privacy. Discover how FAMOS addresses critical challenges in mobile payment security by fusing multi-modal sensor data and clustering user data representations by action category, effectively eliminating background noise interference. Examine the framework's impressive performance metrics, including a 0.91 F1-Score and 0.97 AUC, representing significant improvements of 42.19% and 27.63% over baseline methods. Understand how the integration of federated learning not only maintains user privacy but also improves the system's overall effectiveness in real-world deployment scenarios.
FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning
Overview
Syllabus
USENIX Security '24 - FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via...
Taught by
USENIX
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