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Record W1907236072 · doi:10.1103/physreva.94.030302

Creation of backdoors in quantum communications via laser damage

2016· article· en· W1907236072 on OpenAlexafffund
Vadim Makarov, Jean Philippe Bourgoin, Poompong Chaiwongkhot, Mathieu Gagné, Thomas Jennewein, Sarah Kaiser, Raman Kashyap, Matthieu Legré, Carter Minshull, Shihan Sajeed

Bibliographic record

VenuePhysical review. A/Physical review, A · 2016
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsCanadian Institute for Advanced ResearchPolytechnique MontréalUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOffice of Naval ResearchIndustry CanadaCanada Foundation for Innovation
KeywordsComputer scienceQuantum key distributionComputer securityKey (lock)LaserCoin flippingQuantumComputer networkPhysicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Practical quantum communication (QC) protocols are assumed to be secure provided implemented devices are properly characterized and all known side channels are closed. We show that this is not always true. We demonstrate a laser-damage attack capable of modifying device behavior on demand. We test it on two practical QC systems for key distribution and coin tossing, and show that newly created deviations lead to side channels. This reveals that laser damage is a potential security risk to existing QC systems, and necessitates their testing to guarantee security.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.360
Teacher spread0.340 · 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 designBench or experimental
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

Citations84
Published2016
Admission routes2
Has abstractyes

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