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Record W3030674378

Secret Keys from Channel Noise.

2011· preprint· en· W3030674378 on OpenAlexaff
Hadi Ahmadi, Reihaneh Safavi–Naini

Bibliographic record

VenueIACR Cryptology ePrint Archive · 2011
Typepreprint
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceRandomnessInitializationChannel (broadcasting)Upper and lower boundsBinary symmetric channelAlice and BobKey (lock)Computer networkProtocol (science)Binary numberComputer securityVerifiable secret sharingAdversaryTheoretical computer scienceChannel capacityAlice (programming language)MathematicsArithmeticStatisticsSet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

Abstract. We study the problem of unconditionally secure Secret Key Establishment (SKE) when Alice and Bob are connected by two noisy channels in opposite directions, and the channels are eavesdropped by Eve. We consider the case that Alice and Bob do not have any sources of initial randomness at their disposal. We start by discussing special cases of interest where SKE is impossible, and then provide a simple SKE construction over a binary symmetric channel that achieves some rates of secret key. We next focus on the Secret Key (SK) capacity, i.e., the highest rate of secure and reliable key establishment (in bits per channel use) that the parties can achieve. Relying on the existence of capacity-achieving coding schemes, we propose a multi-round SKE protocol, called the main protocol, that proves a lower bound on the SK capacity. The main protocol consists of an initialization round, followed by repeated use of a two-round SKE protocol, called the basic protocol. We also provide an upper bound on the SK capacity and show that the two bounds coincide when channels do not leak information to the adversary. We apply the results to the case that communicants are connected by binary symmetric channels. 1

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.241
Teacher spread0.214 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations0
Published2011
Admission routes1
Has abstractyes

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