Enforcing patient privacy in healthcare WSNs through key distribution algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Patient data privacy, as one of the foremost security concerns in healthcare applications, must be enforced through the use of strong cryptography. However, in the scenario where the patient wears a body network in which lightweight, battery‐operated wireless sensors monitor various health variables of interest, the requirements for strong cryptography must often be balanced against the requirements for energy efficiency. In this paper, we describe two algorithms for key distribution. The first algorithm relies on a central trusted security server (CTSS) to authenticate that participants indeed belong to the patient's group and to generate the session key. In the second algorithm, participants authenticate each other using certificates and are largely independent of the central trusted security server (CTSS); this algorithm uses elliptic curve cryptography (ECC) to reduce energy consumption by cryptographic computations. In both cases, the patient's security processor has a lead role in authenticating group membership and the key generation process. Using the data from commercial devices compliant with the IEEE 802.15.4 low data rate WPAN technology, we show that this approach can be successfully implemented in networks built with low power motes. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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