Concurrent error detection of polynomial basis multiplication over extension fields using a multiple-bit parity scheme
Why this work is in the frame
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Bibliographic record
Abstract
Cryptographic systems implemented using VLSI technologies require a large number of circuits and are prone to faults. Attacks on cryptosystems that exploit erroneous results due to faults in hardware have recently been reported in the literature. As a result, the detection and correction of errors in cryptographic operations have become an important issue. This paper discusses the detection of multiple-bit faults in bit-serial and bit-parallel polynomial basis multipliers over binary extension fields. Our approach is based on multiple-bit parity. Results show that due to an increase in the number of parity bits, area overhead increases linearly, but the probability of error detection approaches unity sharply so that it reaches 0.95 for 6 parity bits.
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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 | 0.000 | 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