Security of BLS and BGLS signatures in a multi-user setting
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it