A High-Performance Fault Diagnosis Approach for the AES SubBytes Utilizing Mixed Bases
Bibliographic record
Abstract
The Sub Bytes (S-boxes) is the only non-linear transformation in the encryption of the Advanced Encryption Standard (AES), occupying more than half of its hardware implementation resources. One important required aspect of the hardware architectures of the S-boxes is the reliability of their implementations. This can be compromised by occurrence of internal faults or intrusion of the attackers. In this paper, we present a high-speed architecture for the S-boxes constructed using mixed bases to counteract these internal/malicious faults. Although using polynomial and normal bases for the S-boxes has been studied extensively, using mixed bases has just been considered very recently in CHES 2010. In the proposed fault detection scheme of this paper, we present formulations for multi-bit parities for the S-boxes using mixed bases. Then, these formulations are utilized in our error simulations and it is shown that the presented architecture reaches very high error coverage. Through our ASIC syntheses utilizing a 65-nm CMOS technology, we show that with comparable hardware complexity, the efficiency of the presented reliable architecture (without sub-pipelining) reaches around 5.02 Mbps/μm2, outperforming other fault detection schemes for composite field architectures.
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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 | 0.000 | 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 | 0.000 |
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".