Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".