Indistinguishability and Non-deterministic Encryption of the Quantum Safe Multivariate Polynomial Public Key Cryptographic System
Bibliographic record
Abstract
Multivariate Polynomial Public Key (MPPK) is a cryptographic system, over a prime Galois field. A key pair is generated using a multiplier multivariate polynomial and two multiplicand univariate solvable polynomials. They yield two product multivariate polynomials. The first variable is used for carrying the message or secret and others are used as noise sources. The public key consists of all the coefficients of the product multivariate polynomials, except the two constant coefficients, in terms with coefficients attached to the message variable, and a noise function or a polynomial of only noise variables generated from the constant term of the multiplier multivariate polynomial by multiplying a private random variable R. The private key is made of both univariate solvable multiplicand polynomials and the private R. Encryption takes a secret message and random numbers for noises, adding noise that is automatically cancelled by decryption. Decryption is achieved evaluating a solvable equation. We review security analysis that can be employed to crack MPPK secrets and private keys. Finally, we discuss indistinguishability and non-deterministic encryption, key properties of MPPK.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".