Multi-Issuer Attribute-based Anonymous Credential with Traceability and Revocation
Bibliographic record
Abstract
Attribute-based anonymous credential schemes allow users to obtain credentials from the issuer and prove the possession of their attributes interactively and anonymously with service providers. So far, most existing schemes only consider single-issuer, where some of them are extended to be traceable or revocable. In the reality, anonymous credential schemes under multi-issuer are more practical, since users could query for credentials from different issuers and use some of them simultaneously, which is more efficient than showing them individually. Although there are also multi-issuer schemes where users obtain credentials from different issuers and show an aggregated credential to service providers, however, these schemes lack practical properties, for example, revocation of invalid users. In this paper, we propose a multi-issuer attribute-based anonymous credential with traceability and revocation, which provides traceability of invalid users, and revocation of the specific users. Users receive credentials from multiple issuers and show an aggregated credential of selective disclosures of attributes. We provide the security model of our scheme of anonymity and unforgeability. Finally, we discuss the computational complexity, which shows the practicality and efficiency of our scheme.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".