Analysis of Zero-Key Authentication and Zero-Knowledge Proof
Bibliographic record
Abstract
Recently passwordless authentication such as zero-key authentication or zero-knowledge access control is becoming popular among businesses prioritizing their users' and employees' security and digital experience. A challenge-response mechanism and public key infrastructure (PKI) cryptography are employed to perform the zero-key authentication or zero- knowledge access control that authorizes user access to an online service without a password or any shared secret required. Using a large quantum computer, a quantum algorithm could break the hard mathematical problems underlying PKI. The National Institute of Standards and Technology (NIST) has launched a program and competition to standardize one or more post-quantum cryptographic (PQC) algorithms to fight against quantum attacks. In this paper, we have conducted the first-ever mathematical analysis of lattice-based and polynomial-based PQC by introducing the relationship between automorphism and homomorphism. This analysis can help enterprises and organizations leverage NIST-selected PQC algorithms to safeguard their online services from quantum attacks. We performed the simulation to illustrate brute force broken probability for polynomial-based or multivariate-based PQC to validate our mathematical analysis of PQC.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".