Benchmark Performance of a New Quantum-Safe Multivariate Polynomial Digital Signature Algorithm
Bibliographic record
Abstract
Kuang et al. introduced the new quantum-safe algorithm Multivariate Polynomial Public Key Digital Signature (MPPK DS). To create a signature, the MPPK DS scheme’s private key consists of univariate polynomials used as exponents of a secret randomly generated base. For signature verification, the verifier leverages public key multivariate polynomials and a modular arithmetic property. The verification procedure is probabilistic. The verifier uses noise variables and evaluates the public key polynomials. For a genuine signature, the verification procedure is successful for any evaluation of the public key polynomials. In this paper, we report the results of benchmarking MPPK DS on a 16-core Intel®Core™i7-10700 CPU system at 2.90 GHz using the SUPERCOP toolkit. SUPERCOP has been widely used to analyze the performance of post-quantum public-key encryption and key-establishment algorithms. We provide a side-by-side comparison of the NIST PQC third-round digital signature schemes with MPPK DS. With respect to the PQC schemes, the MPPK DS cryptosystem achieves small size public keys, private keys, and signatures. Moreover, compared with the NIST PQC digital signature algorithms, the performance of the MPPK DS algorithm is outstanding with fast procedures for key generation, signing, and verifying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".