Finite-key-size effect in a commercial plug-and-play QKD system
Bibliographic record
Abstract
Abstract A security evaluation against the finite-key-size effect was performed for a commercial plug-and-play quantum key distribution (QKD) system. We demonstrate the ability of an eavesdropper to force the system to distill key from a smaller length of sifted-key. We also derive a key-rate equation that is specific for this system. This equation provides bounds above the upper bound of secure key under finite-key-size analysis. From this equation and our experimental data, we show that the keys that have been distilled from the smaller sifted-key size fall above our bound. Thus, their security is not covered by finite-key-size analysis. Experimentally, we could consistently force the system to generate the key outside of the bound. We also test manufacturer’s software update. Although all the keys after the patch fall under our bound, their security cannot be guaranteed under this analysis. Our methodology can be used for security certification and standardization of QKD systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".