Privacy-preserving Scheme using Secure Group Communication for M-healthcare Information Systems
Bibliographic record
Abstract
Abstract The expeditious growth of the wearable and implantable body sensors and wireless communication technologies have provided both inspiration and motivation for increasingly development of m-healthcare information systems as a promising next generation e-health system. In m-healthcare systems, the authorized mobile patients with the same disease symptoms can constitute a social group to share their health condition and medical experience. The privacy of social communication transferred over open wireless channels is an essential system requirement. Furthermore, the m-healthcare system on contrary to the traditional e-Health system allows mobile patients to move across distinguished location domains during different time periods. The mobility of patients considerably increases the cost of key management in terms of communication overhead if it is addressed with a naïve solution such as treating as a leave in the old location and a new join in the visited location. This paper proposes a privacy-preserving scheme, which maintains the secrecy of patients’ personal health information using secure group communication in m-healthcare information systems while supporting mobility of patients. The scheme is highly scalable, and treats patients’ mobility with the minimum rekeying cost, as such efficiently preserve secrecy of communication between patients associated with a social group. The security properties of the proposed scheme as well as its performance based on simulation experiments are evaluated. The experimental results demonstrate that the proposed scheme outperforms the existing solution in terms of communication overhead.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".