Analysis of Quarter Rounds of Salsa and Chacha Core and Proposal of an Alternative Design to Maximize Diffusion
Bibliographic record
Abstract
Background/Objectives: Salsa and ChaCha are commonly used encryption primitives. Both Salsa and ChaCha core use Quarter round as its core function. The objective of the paper is to analyze the diffusion property of Quarter round of both these algorithms and propose an alternative design named Modified ChaCha Core (MCC). Methods: The Quarter round functions of all these three algorithms are compared using the diffusion matrices that reflect change in output words with a small change in input words. For each algorithm we generated more than a million diffusion matrices depending on the possible permutations of rotations constants used in Quarter round. Findings: Results of our experiment reflected that for Salsa and ChaCha core, there are high number of alternative rotation constants that generate more diffusion than the rotation constants prescribed by the authors. The comparison of diffusion matrices of all three competing structures also concluded that quarter round of MCC exhibits more diffusion than Quarter round of Salsa and ChaCha and it does so in lesser operations. Applications: MCC core; the design proposed in this paper, may be used to generate stream ciphers or may be used to generate collision resistant compression function for a cryptographic hash algorithm. Keywords: ChaCha, Diffusion, Modified ChaCha, MCC, Salsa, Stream Ciphers
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".