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The Secrecy Capacity of Gaussian Wiretap Channels with Rate-Limited Help at the Encoder

2023· article· en· W4382365049 on OpenAlexaff
Sergey Loyka, Neri Merhav

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSecrecyTransmitterEncoderComputer scienceCoding (social sciences)Computer networkChannel capacityChannel (broadcasting)Computer securityGaussianTelecommunicationsTopology (electrical circuits)MathematicsStatisticsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

The Gaussian wiretap channel (WTC) with rate-limited help, available at the transmitter/encoder (Tx), in addition to or instead of the same help at the legitimate receiver, is studied under various channel configurations. For the degraded or reversely-degraded WTC, rate-limited non-secure Tx help results in a secrecy capacity boost equal to the help rate irrespective of whether the help is causal or not. For the non-degraded WTC, the secrecy capacity boost is lower bounded by the help rate. A capacity-achieving signaling is two-phase time sharing, where wiretap coding without help is used in Phase 1 and help without wiretap coding is used in Phase 2. The secrecy capacity with Tx help is positive for the reversely-degraded channel (where the no-help secrecy capacity is zero) and no Phase 1 is needed to achieve it. Unlike the no-help case, more noise at the legitimate receiver can sometimes result in higher secrecy capacity with Tx help. In the case of the joint Tx/Rx non-secure help, one help link can be omitted without affecting the capacity.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.232
Teacher spread0.207 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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