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Record W2542738598 · doi:10.1088/2058-9565/aa804b

Finite-key-size effect in a commercial plug-and-play QKD system

2017· article· en· W2542738598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuantum Science and Technology · 2017
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQuantum key distributionKey (lock)StandardizationCertificationUpper and lower boundsSoftwareCryptography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.250
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it