Electrocardiogram Based Group Device Pairing for Wearables
Bibliographic record
Abstract
The widespread usage of wearables to provide healthcare services prompts the need for secure group communication among multiple devices using group keys. Gait-based group key establishment schemes are either vulnerable to video attacks, or fail to offer a secure group key update mechanism when group device changes. In this paper, we present an electrocardiogram (ECG) signals based group device pairing protocol, which can strengthen the security and reduce the overhead of wearables. Specifically, we first design a robust and lightweight fuzzy extractor that supports secure and efficient group device association between wearables. Meanwhile, we propose Improved Martingale Randomness Extraction (IMRE) algorithm, which utilizes the trend of InterPulse Interval (IPI) from ECG signal to extract high-entropy keys. Then we present a membership management mechanism that enables group key dynamic update when group device changes. Finally, we simulate our protocol and evaluate the accuracy and efficiency by various experiments. The experimental results demonstrate that the proposed work is robust and efficient, and the threat model-based security analysis shows that the proposed protocol can prevent both active and passive attacks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".