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Record W2024310614 · doi:10.1515/jmc.2008.007

Improved security analysis of PMAC

2008· article· en· W2024310614 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

VenueJournal of Mathematical Cryptology · 2008
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
Fundersnot available
KeywordsPMACParallelizable manifoldComputer scienceMessage authentication codeUpper and lower boundsAuthentication (law)Provable securityTheoretical computer scienceAlgorithmMathematicsCryptographyComputer security

Abstract

fetched live from OpenAlex

Abstract In this paper we provide a simple, concrete and improved security analysis of Parallelizable Message Authentication Code or PMAC. In particular, we show that the advantage of any distinguisher at distinguishing PMAC from a random function is at most (5 q σ – 3.5 q 2 )/2 n . Here, σ is the total number of message blocks in all q queries made by and PMAC is based on a random permutation over {0, 1} n . In the original paper of PMAC by Black and Rogaway in Eurocrypt-2002, the bound was shown to be (σ + 1) 2 /2 n –1 . In FSE-2007, Minematsu and Matsushima provided a bound 5ℓ q 2 /(2 n – 2ℓ), where ℓ is the number of blocks of the longest queried made by the distinguisher. Our proposed bound is sharper than these two previous bounds.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.464
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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