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Touch Authentication for Sharing Context Using Within-Group Similarity Structure
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PRE
AI
DOI：10.1109/JIOT.2024.3402323.png)
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摘要
En 中文
Sharing digital resources is a common practice in both work and personal life. Yet, sharing identical credentials, such as passwords or physical cards, not only poses significant security risks but also falls short in addressing the specific requirements of small local groups, such as parental controls, tracking user modifications, and easily updating access. To address this, we suggest a touch behavior-based method tailored for sharing in small local groups, designed to balance between ensuring relaxed security and maintaining practical functionality. Our approach aims to concurrently identify in-group users and detect out-of-group imposters. Specifically, our approach extracts effective identity representations that are robust to in-group variability and out-of-group uncertainty by learning a pair of touch-behavioral and within-group similarity embeddings. While the former captures the unique features of user touch characteristics, the latter reflects the typical group-wide similarity structure that an in-group user is expected to possess from a holistic perspective. Experimental results showcase the effectiveness of our method even with few samples for training. It maintains accuracy despite the group growing larger and shows resilience against the advanced attacks. This offers a promising way to keep group access both user-friendly and relatively secure, striking a crucial balance for small groups' needs.
Keyword:
Authentication
Passwords
Training
Security
Internet of Things
Privacy
Object recognition
behavioral biometrics
touch behavior
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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