Enhancing Security and Efficiency in IoT Assistive Technologies: A Novel Hybrid Systolic Array Multiplier for Cryptographic Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
The incorporation of Internet of Things (IoT) edge nodes into assistive technologies greatly improves the daily lives of individuals with disabilities by facilitating real-time data processing and seamless connectivity. However, the increasing adoption of IoT edge devices intended for individuals with disabilities presents significant security challenges, particularly concerning the safeguarding of sensitive data and the heightened risk of cyber vulnerabilities. To effectively mitigate these risks, advanced cryptographic protocols, including those based on elliptic curve cryptography, have been proposed to establish robust security measures. While these protocols are effective in reducing the risk of data exposure, they often demand considerable computational resources, which poses challenges for cost-effective IoT devices. Therefore, it is essential to prioritize the effective execution of cryptographic algorithms, as they rely on finite field operations such as multiplication, inversion, and division. Among these computations, field multiplication is particularly critical, serving as the backbone for the other operations. This study intends to create an innovative hybrid systolic array design for the Dickson basis multiplier, which integrates both serial and parallel inputs to enhance overall performance. The proposed design is anticipated to significantly reduce space and power consumption, thereby enabling the secure execution of complex cryptographic algorithms on resource-limited IoT devices designed for disabled people. By addressing these pressing security issues, the study aspires to fully leverage IoT technologies to enhance the living standards of individuals with disabilities, while ensuring that their privacy and security are meticulously maintained.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it