A Quantum-Corrected Chaotic System for Strengthening Schnorr and Elgamal Signatures to Optimize Key Generation and Performance
Bibliographic record
Abstract
This paper aims to improve the performance of Schnorr and Elgamal schemes using the random property of the chaotic maps. After analyzing different chaotic map types, this paper generates a new 1D chaotic system based on sin and logistic maps. After that, we derived a new map with quantum corrections by coupling a kicked quantum system with a harmonic oscillator bath. As the dissipation parameter increases, we observe a period-doubling progression towards classical behavior, along with other intriguing characteristics at intermediate parameter values. Then, this new chaotic system is applied to the Schnorr and Elgamal schemes. Extensive randomness tests were conducted, and the algorithm demonstrated exceptional performance. Following rigorous testing and analysis, the algorithm exhibited impressive signing and verification times of approximately 0.0000799212 (s) and 0.0000100223 (s) for the Schnorr scheme, and 0.000029932 (s) and 0.0000399298 (s) for the Elgamal scheme, respectively. These times are notably lower compared to other proposed algorithms. The private key space was expanded to from , further strengthening security. Testing with 100,000 messages of varying lengths confirmed the algorithm's robust performance, making it a viable option for contemporary cryptosystems used in multimedia data exchange.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".