Bibliographic record
Abstract
Randomizing the execution of the sequence of operations in an algorithm is one of the most frequently considered solutions to improve the security of cryptographic implementations against sidechannel analysis. Such an algorithm for public-key cryptography was introduced by Tunstall at ACISP, 2009. In his right-to-left m-ary exponentiation algorithm, the radix-m digits of the exponent are treated in somewhat random order. This randomized solution will inhibit attacks that allow operations to be distinguished from one acquisition. In this article, we present a memory-efficient variant of Tunstall's random-order exponentiation algorithm, making it applicable to modular exponentiations in (Z/NZ)* (for instance, the RSA cryptosystem). The proposed algorithm requires only (m+1) memory registers instead of (m+r), where r > m as recommended in Tunstall's algorithm. Namely, the proposed algorithm saves about half the memory registers. Our analysis shows that our algorithm can be used as a supplement in order to defeat statistical side-channel analysis attacks, especially recent collision-correlation power analysis in the horizontal setting. Last but not least, we present a random order binary implementation, which is the first right-to-left binary implementation resisting attacks in the horizontal setting.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".