Authentication Protocol for Real-Time Wearable Medical Sensor Networks Using Biometrics and Continuous Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The open nature of wireless medical sensor networks in a public untrusted environment makes them vulnerable to various security threats and puts the security and privacy of patient information at risk. This paper introduces a new ECC based lightweight mutual authentication and key agreement protocol to be used in real-time wireless medical sensor networks between doctors/nurses, trusted servers, sensors and patients. Unlike existing schemes, our scheme uses biometrics on both doctor/nurse and patient sides. It allows the doctor/nurse to login to the system using his/her fingerprint and verifies patient identity by means of continuous monitoring of physiological data (e.g., ECG signals) in which verification of the patient identity is carried out automatically and at set intervals to detect physical theft of the sensor which may be hooked on to a different patient. Our scheme also uses dynamic identity to provide user anonymity and mitigate against user traceability. Security analysis shows that our protocol is resistant to the user, sensor and patient impersonation attacks, physical sensor theft, and so on. Performance analysis proved our scheme to be competitive in comparison to existing schemes relative to the added security benefits it provides.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it