Compact modular multiplier design for strong security capabilities in resource-limited Telehealth IoT devices
Bibliographic record
Abstract
Telehealth is an emerging model of delivering quality health to remote communities and stay-at-home users. This is motivated by the rising health care costs and by the benefits of many patients staying at home as opposed to extended hospital stays. Telehealth relies on IoT technology, but IoT devices present the weakest security link to the system. The challenge is to implement strong security capabilities in resource-limited IoT devices. This justifies the use of elliptic curve cryptography (ECC) over the other traditional and resource-consumed approaches such as RSA. Efficient modular multiplication is a basic operation needed for ECC systems. Therefore, the compact and efficient implementation of this operation will significantly affect the performance of ECC in resource-limited applications. This work presents a compact serial-in/serial-out word-based systolic implementation of modular multiplication. The proposed structure is derived using a formal and systematic technique for mapping regular iterative algorithms (RIA) onto processor arrays. The proposed methodology enables control of the processor array workload as well as the workload of each processing element. Controlling the processor word size allows control of system speed, latency, and area. The proposed processor structure saves area and energy consumption by a factor up to 96.3% and 98.5%, respectively.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".