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Record W2066040291 · doi:10.1142/s0129626410000193

KEY DISTRIBUTION VERSUS KEY ENHANCEMENT IN QUANTUM CRYPTOGRAPHY

2010· article· en· W2066040291 on OpenAlexaff
Naya Nagy, Marius Nagy, Selim G. Akl

Bibliographic record

VenueParallel Processing Letters · 2010
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsQueen's University
Fundersnot available
KeywordsQuantum cryptographyQuantum key distributionComputer scienceCryptographyKey distributionKey (lock)ID-based cryptographyPublic-key cryptographyTheoretical computer scienceFinancial cryptographyShared secretLattice-based cryptographyPKCS #1Quantum informationComputer securityQuantumEncryptionQuantum mechanicsPhysicsCryptosystem

Abstract

fetched live from OpenAlex

It has been said that quantum cryptography in general offers a secure solution to the problem of key enhancement. This means that two parties who already share a small secret key, can use quantum protocols to establish a new large secret key. This large secret key can be arbitrarily long and is unbreakable. Thus, to date, the main contribution of quantum cryptography has been believed to be quantum key enhancement. This paper shows that quantum cryptography can do significantly more. The quantum protocol described here distributes an unbreakable secret key to the two parties by relying on public information only. This is the first time that quantum cryptography is shown to be able to produce secret information using only public information. This contribution is also unique for cryptography in general, classical and quantum.

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

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.014
Scholarly communication0.0030.015
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2010
Admission routes1
Has abstractyes

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