Multivariate Polynomial Public Key Digital Signature Trefoil Knot Algorithm
Bibliographic record
Abstract
The imminent commercialization of quantum computing technologies poses significant risks to classical encryption algorithms. In response, the National Institute of Standards and Technology is spearheading efforts to standardize robust Post-quantum Cryptography (PQC) algorithms. This study focuses on the Multivariate Polynomial Public Key Digital Signature Trefoil Knot (MPPK/DSTK) algorithm, a notable advancement refactored from recent PQC developments, distinguished by its integration of true random numbers generated by quantum computers. To evaluate its integrity and robustness against deep learning-based brute force attacks, we introduced semi- covariance correlation analysis - a novel assessment method in this context - to explore the algorithm's resilience by potentially narrowing the search space. Our analysis reveals that MPPK/DSTK exhibits superior performance, with lower semi-covariance and enhanced robustness compared to the traditional Rivest-Shamir-Adleman (RSA) public-key cryptosystem, especially with selected seed primes. We have made our developments accessible on GitHub, inviting the research community to engage in further comparative studies and collaborative enhancements. This study underscores the MPPK/DSTK algorithm's potential as a formidable contender in the evolution of cryptography, offering a significant leap forward in securing digital communications against the quantum computing threat.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".