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Record W1993695587 · doi:10.1109/tifs.2013.2293113

Cooperative Key Agreement for Wireless Networking: Key Rates and Practical Protocol Design

2014· article· en· W1993695587 on OpenAlexaff
Ning Wang, Ning Zhang, T. Aaron Gulliver

Bibliographic record

VenueIEEE Transactions on Information Forensics and Security · 2014
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsKey-agreement protocolComputer scienceRelayComputer networkKey (lock)Node (physics)WirelessPre-shared keyWireless networkFadingProtocol (science)Upper and lower boundsBlock (permutation group theory)Key exchangePublic-key cryptographyKey distributionComputer securityChannel (broadcasting)TelecommunicationsEncryptionMathematics

Abstract

fetched live from OpenAlex

In this paper, we investigate the design of a practical information-theoretically secure secret key agreement protocol for a cooperative wireless network employing standard modulation. Assuming relay selection has been completed, the key agreement problem is studied in a three-node cooperative wireless communication system over block-fading channels. Passive attacks from an eavesdropper collocated with the relay are considered. We derive upper and lower bounds on the secret key rate of this cooperative wireless system. The difference between the bounds is shown to be small for practical communication scenarios, which indicates they are tight. We then propose a practical secret key agreement protocol for this system with both the communicants and the honest relay participating in the public discussion. The tradeoff between security and protocol efficiency is considered in the joint design of advantage distillation, information reconciliation, and privacy amplification. The protocol parameters are optimized to achieve the tight bound on the secret key rate.

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)0.013
metaresearch head score (Gemma)0.027
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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0030.007
Open science0.0030.003
Research integrity0.0030.003
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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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
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

Citations35
Published2014
Admission routes1
Has abstractyes

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