Referee report. For: Quantum data communication protection with the quantum permutation pad block cipher in counter mode and Clifford operators [version 1; peer review: 1 approved]
Bibliographic record
Abstract
Background: This article integrates two cryptographic schemes for quantum data protection. The result achieves authentification, confidentiality, integrity, and replay protection. The authentication, integrity, and replay aspects leverage quantum Clifford operators. Confidentiality of quantum messages is achieved using the quantum permutation pad (QPP) cryptographic scheme. Methods: Clifford operators and the QPP are combined into a block cipher in counter mode. A shared secret is used to seed a random number generator for the arbitrary selection of Clifford operators and quantum permutations to produce a signature field and perform encryption. An encryption and signature algorithm and a decryption and authentication algorithm are specified to protect quantum messages. Results: A symmetric key block cipher with authentication is described. The plain text is signed with a sequence of randomly selected Clifford operators. The signed plaintext is encrypted with a sequence of randomly selected permutations. The algorithms are analyzed. As a function of the values selected for the security parameters, there is an unavoidable risk of collision. The probability of block collision is modelled versus the number of blocks encrypted, for block sizes two, three, four, and five qubits. Conclusions: The scheme is practical but does not achieve perfect indistinguishability because of the risk of message collision. This is normal and unavoidable when fixed-size fields are assumed to make a scheme practical. The model can be used to determine the values of the security parameters and the lifetime of session keys to mitigate the risk of information leakage according to the needs of the scheme’s users. The session key can be renewed when a tolerable maximum number of messages has been sent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.473 | 0.177 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".