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Record W2885994734 · doi:10.1109/isit.2018.8437594

Secure Key Agreement over Partially Corrupted Channels

2018· article· en· W2885994734 on OpenAlexafffund
Reihaneh Safavi–Naini, Peng‐Wei Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsAlice and BobAdversaryComputer scienceKey (lock)Disjoint setsChannel (broadcasting)CryptographyShared secretKey exchangeSet (abstract data type)Public-key cryptographyTheoretical computer sciencePre-shared keyComputer networkComputer securityMathematicsDiscrete mathematicsAlice (programming language)Encryption

Abstract

fetched live from OpenAlex

Key agreement (KA) is a fundamental cryptographic primitive. Assuming that Alice and Bob do not have any prior shared correlation, it has been proved that key agreement with security against a computationally unbounded adversary is impossible if communication is either over a fully public channel, or the channel is fully controlled by the adversary. In this paper we consider a setting where communication is over a partially corrupted channel, and there is no prior shared correlation. We formalize security and reliability of key agreement protocols in this setting, derive bounds on the rate of secret key agreement, and give constructions that achieve the respective bounds. Our results show that secret key agreement, and hence secret message transmission, is possible, as long as a small fraction of the transmitted symbols in each round remain untouched by the adversary. Our results can be extended to key agreement between nodes in a network, when the two nodes are connected by a set of disjoint paths, and in each round a subset of paths is eavesdropped and another subset (possibly with overlap) is tampered with. We relate our results to previous works, and discuss future directions.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2018
Admission routes2
Has abstractyes

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