Multivariate Polynomial Public Key Digital Signature Algorithm: Semi-covariance Analysis and Performance Test over 5G Networks
Bibliographic record
Abstract
The arrival of quantum computers is imminent, with no room for uncertainty. It is only a matter of a few years before classical encryption algorithms face significant risks. NIST is leading the world to devise and standardize Post Quantum Cryptography (PQC) algorithms. In this study, we considered the Multivariate Polynomial Public Key (MPPK) Digital Signature (DS) algorithm as one of recent PQC algorithms and generated MPPK/DS key pairs with the true random number from the quantum computer. For each key pair, we calculated the semi-covariance correlation to measure its robustness against brute force attacks. The results show that, in average with the selected seed prime, MPPK has lower semi-covariance and thereby stronger robustness over traditional RSA and SPHINCS algorithms. Additionally, we measured the latency performance of MPPK/DS algorithm over 5G, WiFi, and local area networks, in comparison of RSA and SPHINCS algorithms. Finally, We made all developments available online for research community to carry out similar comparisons with other PQC algorithms.
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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 | 0.003 | 0.015 |
| 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) | 0.001 | 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".