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Insecurity of Detector-Device-Independent Quantum Key Distribution

2016· article· en· W2479771244 on OpenAlexafffund
Shihan Sajeed, Anqi Huang, Shi‐Hai Sun, Feihu Xu, Vadim Makarov, Marcos Curty

Bibliographic record

VenuePhysical Review Letters · 2016
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceMinisterio de Economía y CompetitividadOffice of Naval ResearchCanada Foundation for InnovationNational Natural Science Foundation of ChinaIndustry CanadaFederación Española de Enfermedades Raras
KeywordsQuantum key distributionEavesdroppingDetectorKey (lock)ExploitComputer scienceComputer securityQuantum entanglementSide channel attackChannel (broadcasting)Quantum cryptographyImplementationQuantumComputer networkCryptographyPhysicsTelecommunicationsQuantum informationQuantum mechanics

Abstract

fetched live from OpenAlex

Detector-device-independent quantum key distribution (DDI-QKD) held the promise of being robust to detector side channels, a major security loophole in quantum key distribution (QKD) implementations. In contrast to what has been claimed, however, we demonstrate that the security of DDI-QKD is not based on postselected entanglement, and we introduce various eavesdropping strategies that show that DDI-QKD is in fact insecure against detector side-channel attacks as well as against other attacks that exploit devices' imperfections of the receiver. Our attacks are valid even when the QKD apparatuses are built by the legitimate users of the system themselves, and thus, free of malicious modifications, which is a key assumption in DDI-QKD.

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)0.005
metaresearch head score (Gemma)0.013
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 score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0040.007
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.269
Teacher spread0.254 · 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

Citations64
Published2016
Admission routes2
Has abstractyes

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