ChameleonRev: A Novel Approach to Efficient and Granular Control Credential Revocation on the Blockchain
Bibliographic record
Abstract
Several approaches have been suggested for authenticating verifiable credentials, however, most fail to address the issue of revocation. Additionally, the suggested revocation technique lacks the effectiveness and efficiency required to offer a suitable solution for verifiable credentials. Our research presents ChameleonRev, a novel method of finer granular revoking blockchain-based selective disclosure verifiable credentials. ChameleonRev enhances credential management by introducing a revocation capability to verifiable credentials. In ChameleonRev, the validity status of each credential is stored as a field in the blockchain. To revoke a credential, the issuer utilizes the Chameleon Hash Function (CHF) hash collision generation feature. By modifying the validity status to the ‘revoked’ value while ensuring that the generated hash remains unchanged, the credential is effectively revoked. According to our evaluation of ChameleonRev, revocation is seamlessly integrated into traditional blockchain-based selective disclosure methods for credential verification, adding a negligible amount of computing overhead to both the issuing and verifying processes. Furthermore, through a comparative analysis of the revocation process in ChameleonRev against tree structure-based revocation methods such as Certificate Revocation Trees (CRT), we affirm the superiority of our approach.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".