Bibliographic record
Abstract
Anonymous credential (AC) systems allow users, obtaining a credential on a set of attributes, to anonymously prove ownership of the credential and then to selectively disclose a subset of attributes without leaking any other attributes. Recently, a new type of AC, called keyed-verification anonymous credential (KVAC), has been proposed, which indicates that the credential issuer is also the verifier. Conceptually, the KVAC system is suitable for being used as employee cards, library access cards or eIDs (electronic ID cards). However, since the limited process power of smart cards, most of the existing KVAC systems are hard to be implemented on them. In addition, none of the existing KVAC systems provide traceability to obtain the user’s identity if anyone tries to misbehave with KVAC. In this paper, we present the first efficient and traceable KVAC system designated for smart cards. Our scheme provides the following security properties: unforgeability, anonymity, traceability and unlinkability. To demonstrate the efficiency and feasibility, we present an implementation of our scheme on standard Multos smart cards. The implementation results show that our scheme is efficient enough for practical use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".