A McEliece-type Cryptosystem using a Random Inverse Matrix and an Error Vector with Large Hamming Weight
Bibliographic record
Abstract
The McEliece cryptosystem has emerged as a finalist in Round 4 of the NIST Post-Quantum Cryptography (PQC) competition. The Shor algorithm underscores the potential vulnerability of cryptographic primitives to quantum attacks. The McEliece cryptosystem has been shown to be resistent to these attacks. Currently, no known attack is capable of breaking this cryptosystem in polynomial time. Despite this, the McEliece cryptosystem has received little attention in practical applications primarily due to the large public key size. Recent progress to address this issue has reduced the size of the public key by approximately $\mathbf{3 8 \%}$. This paper introduces a McEliece-type cryptosystem which incorporates a large weight error vector and a random inverse matrix to improve security. A key generation algorithm is presented that employs a random matrix to construct the public and private keys. This increases the difficulty of attacks and allows for smaller key sizes than with the McEliece cryptosystem.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".