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Record W2108739512 · doi:10.1109/glocom.2008.ecp.356

Secret Key Generation and Agreement in UWB Communication Channels

2008· article· en· W2108739512 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceKey generationShared secretLow-density parity-check codeKey (lock)TransceiverComputer networkChannel (broadcasting)Public-key cryptographyDecoding methodsComputer securityWirelessAlgorithmTelecommunicationsEncryption

Abstract

fetched live from OpenAlex

It has been shown that the radio channel impulse response for a pair of legitimate Ultra-wide band (UWB) transceivers can be used to generate secret keys for secure communications. Past proposed secret key generation algorithms under-exploited the available number of secret key bits from the radio channel. This paper proposes a new efficient method for generation of the shared key where the transceivers use LDPC decoders to resolve the differences in their channel impulse response measurements caused by measurement noise. To ensure secret key agreement, a method of public discussion between the two users is performed using the syndrome from Hamming (7,3) binary codes. An algorithm is proposed to check the equality of generated keys for both legitimate users, and ensure error-free secure communication. The security of this algorithm has been verified by AVISPA. Comparisons are performed with previous work on secret key generation and it has been shown that this algorithm reliably generates longer secret keys in standard UWB radio channels.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.236
Teacher spread0.201 · 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