Comparison of selected cryptosystems using single-scale and poly-scale measures
Bibliographic record
Abstract
This paper presents useful measures for comparison of distinct cryptosystems, including (i) the public-key cryptography RSA algorithm, (ii) the elliptic-curve cryptography ElGamal algorithm, (iii) a cryptosystem based on radio background noise (RBN), and (iv) a new cryptosystem based on chaos phenomena in cellular automata. The comparison is based on (i) a single-scale measure (i.e., the marginal probability mass functions (mpmf), and (ii) a poly-scale measure (i.e., the finite-sense stationarity, FSS10). Both comparison approaches use the same plaintext and computational power when testing the four cryptosystems. This paper shows experimentally that the chaos based modular dynamical cryptosystem is (i) strong to single-scale statistical cryptanalysis by leaving no patterns in the ciphertexts, (ii) strong to poly-scale cryptanalysis by having a smaller stationarity window than the alternative cryptosystems, and (iii) faster than the selected algorithms from RSA, ElGamal, and natural sources of randomness (RBN).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".