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Record W3214649231 · doi:10.1088/1367-2630/accf32

Device-independent oblivious transfer from the bounded-quantum-storage-model and computational assumptions

2023· article· en· W3214649231 on OpenAlexafffund
Anne Broadbent, Peter Yuen

Bibliographic record

VenueNew Journal of Physics · 2023
Typearticle
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaAir Force Office of Scientific ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsBounded functionProtocol (science)PhysicsOblivious transferTransfer (computing)QuantumComponent (thermodynamics)Independence (probability theory)Theoretical computer scienceComputer scienceQuantum mechanicsAlgorithmCryptographyMathematicsStatisticsParallel computingMathematical analysis

Abstract

fetched live from OpenAlex

Abstract We present a device-independent protocol for oblivious transfer (DIOT) and analyse its security under the assumption that the receiver’s quantum storage is bounded during protocol execution and that the device behaves independently and identically in each round. We additionally require that, for each device component, the input corresponding to the choice of measurement basis, and the resulting output, is communicated only with the party holding that component. Our protocol is everlastingly secure and, compared to previous DIOT protocols, it is less strict about the non-communication assumptions that are typical from protocols that use Bell inequality violations; instead, the device-independence comes from a protocol for self-testing of a single (quantum) device which makes use of a post-quantum computational assumption.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0040.012
Open science0.0040.009
Research integrity0.0030.006
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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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
Published2023
Admission routes2
Has abstractyes

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