Summary of Model-agnostic Utility-preserving Biometric Information Anonymization, by Chun-fu Chen et al.
Model-Agnostic Utility-Preserving Biometric Information Anonymization
by Chun-Fu Chen, Bill Moriarty, Shaohan Hu, Sean Moran, Marco Pistoia, Vincenzo Piuri, Pierangela Samarati
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the intersection of sensing technologies and machine learning in collecting and utilizing people’s biometric data, including fingerprints, voices, retina/facial scans, gait/motion/gestures. The rapid advancements in these areas enable a wide range of applications, from authentication to health monitoring and sophisticated analytics. However, the use of biometrics raises serious privacy concerns due to their sensitive nature and high risk of leaking sensitive information such as identity or medical conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how new sensing technologies and machine learning are helping us collect and use people’s biometric data, like fingerprints and facial scans. This can help with things like secure login and health monitoring. But it also raises important questions about privacy because this kind of information is very sensitive. It’s like having a key to someone’s identity or medical history. |
Keywords
» Artificial intelligence » Machine learning
