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Record W2949704604 · doi:10.26421/qic7.1-2-3

Time-shift attack in practical quantum cryptosystems

2007· article· en· W2949704604 on OpenAlexaff
Bing Qi, C.-H. F. Fung, Hoi‐Kwong Lo, Fermi Ma

Bibliographic record

VenueQuantum Information and Computation · 2007
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuantum key distributionComputer scienceExploitCryptosystemKey (lock)Synchronization (alternating current)Timing attackQubitSIGNAL (programming language)BB84CryptographyComputer networkComputer securityQuantumQuantum computerQuantum networkPhysicsSide channel attackQuantum mechanicsChannel (broadcasting)

Abstract

fetched live from OpenAlex

Recently, a new type of attack, which exploits the efficiency mismatch of two single photon detectors (SPD) in a quantum key distribution (QKD) system, has been proposed. In this paper, we propose another ``time-shift'' attack that exploits the same imperfection. In our attack, Eve shifts the arrival time of either the signal pulse or the synchronization pulse or both between Alice and Bob. In particular, in a QKD system where Bob employs time-multiplexing technique to detect both bit "0'' and bit "1'' with the same SPD, Eve, in some circumstances, could acquire full information on the final key without introducing any error. In addition, we prove that if Alice and Bob are unaware of our attack, the final key they share is insecure. We emphasize that our attack is simple and feasible with current technology. Finally, we discuss some counter measures against our and earlier attacks.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0020.008
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.296
Teacher spread0.275 · 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 designSimulation or modeling
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

Citations157
Published2007
Admission routes1
Has abstractyes

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