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Record W3105977417 · doi:10.20381/ruor-171

Efficient Simulation for Quantum Message Authentication

2016· book-chapter· en· W3105977417 on OpenAlexaff

Bibliographic record

VenueuO Research (University of Ottawa) · 2016
Typebook-chapter
Languageen
FieldComputer Science
TopicQuantum Information and Cryptography
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceMessage authentication codeAuthentication (law)AdversaryTheoretical computer scienceEncoding (memory)Decoding methodsQuantumContext (archaeology)Code (set theory)Class (philosophy)Computer networkComputer securityAlgorithmCryptographyProgramming languagePhysicsGeographyQuantum mechanics

Abstract

fetched live from OpenAlex

A mix of physics, mathematics, and computer science, the study of quantum information seeks to understand and utilize the information that can be held in the state of a quantum system. Quantum cryptography is then the study of various cryptographic protocols on the information in a quantum system. One of the goals we may have is to verify the integrity of quantum data, a process called quantum message authentication. In this thesis, we consider two quantum message authentication schemes, the Clifford code and the trap code. While both of these codes have been previously proven secure, they have not been proven secure in the simulator model, with an efficient simulation. We offer a new class of simulator that is efficient, so long as the adversary is efficient, and show that both of these codes can be proven secure using the efficient simulator. The efficiency of the simulator is typically a crucial requirement for a composable notion of security. The main results of this thesis have been accepted to appear in the Proceedings of the 9th International Conference on Information Theoretic Security (ICITS 2016).

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)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.303
Teacher spread0.244 · 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
GenreOther

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

Citations16
Published2016
Admission routes1
Has abstractyes

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