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Record W2761953145 · doi:10.1109/infocom.2017.8057120

Interactive message transmission over adversarial wiretap channel II

2017· article· en· W2761953145 on OpenAlexaff
Pengwei Wang, Reihaneh Safavi–Naini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceChannel (broadcasting)Computer networkAdversaryAdversarial systemCode wordTransmitterJammingProtocol (science)Computer securityWirelessSecure communicationEavesdroppingTransmission (telecommunications)Alice and BobShared secretWireless sensor networkTheoretical computer scienceCryptographyDecoding methodsTelecommunicationsAlice (programming language)

Abstract

fetched live from OpenAlex

In Wyner wiretap II model of communication, Alice and Bob are connected by a channel that can be eavesdropped by an adversary with unlimited computation who can select a fraction of communication to view, and the goal is to provide perfect information theoretic security. Information theoretic security is increasingly important because of the threat of quantum computers that can effectively break algorithms and protocols that are used in today's public key infrastructure. We consider interactive protocols for wiretap II channel with active adversary who can eavesdrop and add adversarial noise to the eavesdropped part of the codeword. These channels capture wireless setting where malicious eavesdroppers at reception distance of the transmitter can eavesdrop the communication and introduce jamming signal to the channel. We derive a new upperbound R ≤ 1 - ρ for the rate of interactive protocols over two-way wiretap II channel with active adversaries, and construct a perfectly secure protocol family with achievable rate 1 - 2ρ + ρ2. This is strictly higher than the rate of the best one round protocol which is 1 - 2ρ, hence showing that interaction improves rate. We also prove that even with interaction, reliable communication is possible only if ρ <; 1/2. An interesting aspect of this work is that our bounds will also hold in network setting when two nodes are connected by n paths, a ρ of which is corrupted by the adversary. We discuss our results, give their relations to the other works, and propose directions for future work.

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)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.002
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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