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Record W2061474925 · doi:10.1109/tifs.2015.2428211

Secret Key Generation Using Chaotic Signals Over Frequency Selective Fading Channels

2015· article· en· W2061474925 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.

Bibliographic record

VenueIEEE Transactions on Information Forensics and Security · 2015
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFadingComputer scienceChaoticAdditive white Gaussian noiseFrequency domainRandomnessKey (lock)Multipath propagationAlgorithmWirelessChannel (broadcasting)Electronic engineeringTelecommunicationsMathematicsArtificial intelligenceStatisticsEngineering

Abstract

fetched live from OpenAlex

This paper presents a practical key generation algorithm based on the reciprocity of wireless fading channels. A broadband chaotic signal is employed for transmission so that the fading is frequency selective. In this case, signal components in the frequency domain spaced greater than the coherence bandwidth of the channel can be considered uncorrelated. The proposed algorithm exploits this property to generate a unique shared key between two parties. The nonperiodicity of the chaotic signal provides a unique signal for key generation, which can be used even with static fading channels. The proposed approach is robust to timing differences between the parties because the frequency spectrum of the signals is employed. A technique for information reconciliation is presented which does not reveal any information about the values used to generate the key. The randomness of the key is confirmed, and the effects of additive white Gaussian noise and timing differences on the performance of the algorithm are examined.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.254
Teacher spread0.219 · 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