A High-Performance Fault Diagnosis Approach for the AES SubBytes Utilizing Mixed Bases
Why this work is in the frame
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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/μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , outperforming other fault detection schemes for composite field architectures.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it