Concurrent Error Detection in Montgomery Multiplication over Binary Extension Fields
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Bibliographic record
Abstract
Multiplication is one of the most important operations in finite field arithmetic. It is used in cryptographic and coding applications, such as elliptic curve cryptography and Reed-Solomon codes. In this paper, we consider the finite field multiplication used in elliptic curve cryptography and design concurrent error detection circuits. It is shown in the literature that the Montgomery multiplication can be used in cryptography to accelerate the scalar multiplication. Here, we use a parity-based concurrent error detection approach to increase the reliability of different Montgomery multipliers available in the literature. First, we consider bit-serial Montgomery multiplication and propose an error detection circuit. Then, we apply the same technique on the digit-serial Montgomery multiplication. Finally, we consider low time-complexity bit-parallel Montgomery multiplication and design the required components to implement the concurrent error detection circuits. ASIC implementations have been completed to analyze the time and area overheads of the proposed schemes. Also, the error detection capability is investigated by software simulations. We show that our approach results in efficient error detection schemes with small time and area overheads.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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