Making Existential-Unforgeable Signatures Strongly Unforgeable in the\n Quantum Random-Oracle Model
Bibliographic record
Abstract
Strongly unforgeable signature schemes provide a more stringent security\nguarantee than the standard existential unforgeability. It requires that not\nonly forging a signature on a new message is hard, it is infeasible as well to\nproduce a new signature on a message for which the adversary has seen valid\nsignatures before. Strongly unforgeable signatures are useful both in practice\nand as a building block in many cryptographic constructions.\n This work investigates a generic transformation that compiles any\nexistential-unforgeable scheme into a strongly unforgeable one, which was\nproposed by Teranishi et al. and was proven in the classical random-oracle\nmodel. Our main contribution is showing that the transformation also works\nagainst quantum adversaries in the quantum random-oracle model. We develop\nproof techniques such as adaptively programming a quantum random-oracle in a\nnew setting, which could be of independent interest. Applying the\ntransformation to an existential-unforgeable signature scheme due to Cash et\nal., which can be shown to be quantum-secure assuming certain lattice problems\nare hard for quantum computers, we get an efficient quantum-secure strongly\nunforgeable signature scheme in the quantum random-oracle model.\n
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".