Using Vibration for Secure Pairing With Implantable Medical Devices: Development and Usability Study
Bibliographic record
Abstract
Background: Implantable medical devices (IMDs), such as pacemakers, increasingly communicate wirelessly with external devices. To secure this wireless communication channel, a pairing process is needed to bootstrap a secret key between the devices. Previous work has proposed pairing approaches that often adopt a "seamless" design and render the pairing process imperceptible to patients. This lack of user perception can significantly compromise security and pose threats to patients. Objective: The study aimed to explore the use of highly perceptible vibrations for pairing with IMDs and aim to propose a novel technique that leverages the natural randomness in human motor behavior as a shared source of entropy for pairing, potentially deployable to current IMD products. Methods: A proof of concept was developed to demonstrate the proposed technique. A wearable prototype was built to simulate an individual acting as an IMD patient (real patients were not involved to avoid potential risks), and signal processing algorithms were devised to use accelerometer readings for facilitating secure pairing with an IMD. The technique was thoroughly evaluated in terms of accuracy, security, and usability through a lab study involving 24 participants. Results: Our proposed pairing technique achieves high pairing accuracy, with a zero false acceptance rate (indicating low risks from adversaries) and a false rejection rate of only 0.6% (1/192; suggesting that legitimate users will likely experience very few failures). Our approach also offers robust security, which passes the National Institute of Standards and Technology statistical tests (with all P values >.01). Moreover, our technique has high usability, evidenced by an average System Usability Scale questionnaire score of 73.6 (surpassing the standard benchmark of 68 for "good usability") and insights gathered from the interviews. Furthermore, the entire pairing process can be efficiently completed within 5 seconds. Conclusions: Vibration can be used to realize secure, usable, and deployable pairing in the context of IMDs. Our method also exhibits advantages over previous approaches, for example, lenient requirements on the sensing capabilities of IMDs and the synchronization between the IMD and the external device.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".