Strengthened 32‐bit AES implementation: Architectural error correction configuration with a new voting scheme
Bibliographic record
Abstract
Abstract Digital data transmission is day by day more vulnerable to both malicious and natural faults. With an aim to assure reliability, security and privacy in communication, a low‐cost fault resilient architecture for Advanced Encryption Standard (AES) is proposed. In order not to degrade the reliability of our AES architecture, the reliability of voter is very important, for which reason we have introduced a novel voting scheme include a majority voter (named TMR voter) and an error barrier element (named DMR voter). In this paper, a reliable and secure 32‐bit data‐path AES implementation based on our robust fault resilient approach is developed. We illustrate that the proposed architecture can tolerate up to triple‐bit (byte) simultaneous faults at each pipeline stage’s logic and verify our claim through extensive error simulations. Error simulation results also show that our architecture achieves close to 100% fault‐masking capability for multiple‐bit (byte) faults. Finally, it is shown that the Application‐Specific Integrated Circuit implementation of the fault‐tolerant architectures using the composite field‐based S‐box, CFB‐AES, and ROM‐based S‐box, RB‐AES allows better area usage, throughput and fault resilience trade‐off compared to their counterparts. So, it provides the most appropriate features to be used in highly‐secure resource‐constraint applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".