Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908395068456960 |
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| author | Chamon, Christiana Sarkar, Abhijit Abbott, A. Lynn |
| author_facet | Chamon, Christiana Sarkar, Abhijit Abbott, A. Lynn |
| contents | Wearable and implantable healthcare sensors are pivotal for real-time patient monitoring but face critical challenges in power efficiency, data security, and signal noise. This paper introduces a novel platform that leverages hardware noise as a dual-purpose resource to enhance machine learning (ML) robustness and secure data via Physical Unclonable Functions (PUFs). By integrating noise-driven signal processing, PUFbased authentication, and ML-based anomaly detection, our system achieves secure, low-power monitoring for devices like ECG wearables. Simulations demonstrate that noise improves ML accuracy by 8% (92% for detecting premature ventricular contractions (PVCs) and atrial fibrillation (AF)), while PUFs provide 98% uniqueness for tamper-resistant security, all within a 50 uW power budget. This unified approach not only addresses power, security, and noise challenges but also enables scalable, intelligent sensing for telemedicine and IoT applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs Chamon, Christiana Sarkar, Abhijit Abbott, A. Lynn Signal Processing Wearable and implantable healthcare sensors are pivotal for real-time patient monitoring but face critical challenges in power efficiency, data security, and signal noise. This paper introduces a novel platform that leverages hardware noise as a dual-purpose resource to enhance machine learning (ML) robustness and secure data via Physical Unclonable Functions (PUFs). By integrating noise-driven signal processing, PUFbased authentication, and ML-based anomaly detection, our system achieves secure, low-power monitoring for devices like ECG wearables. Simulations demonstrate that noise improves ML accuracy by 8% (92% for detecting premature ventricular contractions (PVCs) and atrial fibrillation (AF)), while PUFs provide 98% uniqueness for tamper-resistant security, all within a 50 uW power budget. This unified approach not only addresses power, security, and noise challenges but also enables scalable, intelligent sensing for telemedicine and IoT applications. |
| title | Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2506.05135 |