Efficient architectures for modular exponentiation using Montgomery powering ladder
Bibliographic record
Abstract
Side channel attacks have been considered as serious threats to certain public-key cryptosystems, such like RSA and elliptic curve system. For modular exponentiation for RSA and scalar multiplication for elliptic curve cryptosystems, Montgomery powering ladder has been shown to be a good choice for counter-measures against side-channel attacks. In this paper, two efficient architectures for modular exponentiation respectively using Montgomery powering ladder algorithm and m-ary powering ladder method are proposed. The first one is a straightforward and efficient implementation of the Montgomery powering ladder algorithm, in which the multiplication and squaring are performed in parallel during each clock cycle. A novel-designed two-by-two cross-point switch is used to select each ladder step. By parallelizing the Montgomery powering ladder using loop unrolling technique so that the number of loops is reduced by half, a second efficient architecture is proposed that requires only half number of clock cycles compared to the first one. The second proposed architecture realizes the m-ary Montgomery powering ladder for the case that the radix equals to 4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".