Threshold Attribute-Based Signatures and Their Application to Anonymous Credential Systems.
Bibliographic record
Abstract
Abstarct. Inspired by the recent developments in attribute-based encryption, in this paper we propose threshold attribute-based signatures (t-ABS). In a t-ABS, signers are associated with a set of attributes and verification of a signed document against a verification attribute set succeeds if the signer has a threshold number of (at least t) attributes in common with the verification attribute set. A t-ABS scheme enables a signature holder to prove possession of signatures by revealing only the relevant (to the verification attribute set) attributes of the signer, hence providing signer-attribute privacy for the signature holder. We define t-ABS schemes, formalize their security and propose two t-ABS schemes: a basic scheme that is selectively unforgeable and a second one that is existentially unforgeable, both provable in the standard model, assuming hardness of the computational Diffie-Hellman problem. We show that our basic t-ABS scheme can be augmented with two extra protocols that are used for efficiently issuing and verifying t-ABS signatures on committed values. We call the augmented scheme a threshold attribute based c-signature scheme (t-ABCS). We show how a t-ABCS scheme can be used to realize a secure threshold attribute-based anonymous credential system (t-ABACS) providing signer-attribute privacy. We propose a security model for t-ABACS and give a concrete scheme using t-ABCS scheme. Using the simulation paradigm, we prove that the credential system is secure if the t-ABCS scheme is secure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| 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".