Bibliographic record
Abstract
Traditional single-user security models do not necessarily capture the power of real-world attackers. A scheme that is secure in the single-user setting may not be as secure in the multi-user setting. Inspired by the recent analysis of Schnorr signatures in the multi-user setting, we analyse Boneh-Lynn-Shacham (BLS) signatures and Boneh-Gentry-Lynn-Shacham (BGLS) aggregate signatures in the multi-user setting. We obtain a tight reduction from the security of key-prefixed BLS in the multi-user model to normal BLS in the single-user model. We introduce a multi-user security model for general aggregate signature schemes, in contrast to the original “chosen-key” security model of BGLS that is analogous to the single-user setting of a signature scheme. We obtain a tight reduction from the security of multi-user key-prefixed BGLS to the security of multi-user key-prefixed BLS. Finally, we apply a technique of Katz and Wang to present a tight security reduction from a variant of multi-user key-prefixed BGLS to the computational co-Diffie-Hellman (co-CDH) problem. All of our results for BLS and BGLS use type III pairings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".