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Record W1894212025 · doi:10.1103/physreva.91.032326

Attacks exploiting deviation of mean photon number in quantum key distribution and coin tossing

2015· article· en· W1894212025 on OpenAlexafffund
Shihan Sajeed, I. V. Radchenko, Sarah Kaiser, Jean-Philippe Bourgoin, Anna Pappa, Laurent Monat, Matthieu Legré, Vadim Makarov

Bibliographic record

VenuePhysical Review A · 2015
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOffice of Naval ResearchIndustry CanadaEuropean CommissionCanada Foundation for Innovation
KeywordsQuantum key distributionBB84Quantum cryptographyComputer sciencePhotonKey (lock)Protocol (science)DetectorCryptographyCryptographic protocolQuantum information scienceQubitCoin flippingTrojan horseEnergy (signal processing)QuantumPhysicsComputer securityMathematicsQuantum mechanicsQuantum informationTelecommunicationsStatisticsQuantum entanglement

Abstract

fetched live from OpenAlex

The security of quantum communication using a weak coherent source requires an accurate knowledge of the source's mean photon number. Finite calibration precision or an active manipulation by an attacker may cause the actual emitted photon number to deviate from the known value. We model effects of this deviation on the security of three quantum communication protocols: the Bennett-Brassard 1984 (BB84) quantum key distribution (QKD) protocol without decoy states, Scarani-Ac\'{\i}n-Ribordy-Gisin 2004 (SARG04) QKD protocol, and a coin-tossing protocol. For QKD we model both a strong attack using technology possible in principle and a realistic attack bounded by today's technology. To maintain the mean photon number in two-way systems, such as plug-and-play and relativistic quantum cryptography schemes, bright pulse energy incoming from the communication channel must be monitored. Implementation of a monitoring detector has largely been ignored so far, except for ID Quantique's commercial QKD system Clavis2. We scrutinize this implementation for security problems and show that designing a hack-proof pulse-energy-measuring detector is far from trivial. Indeed, the first implementation has three serious flaws confirmed experimentally, each of which may be exploited in a cleverly constructed Trojan-horse attack. We discuss requirements for a loophole-free implementation of the monitoring detector.

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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.802
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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 teacher head, 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

Citations81
Published2015
Admission routes2
Has abstractyes

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