Enforcing patient privacy in healthcare WSNs through key distribution algorithms
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".