BAB-SDMM: Blockchain Attribute Based Secure Data Management Model
Bibliographic record
Abstract
The secure access and reliable access revocation methods of modern digital systems are based on access control mechanisms.Access policies, which are used in access control mechanisms, are very important in safeguarding security and ensuring data protection.It is evident that the protection and tamper-proofing of such policies are very important.In addition, efficient access revocation schemes are required to promptly remove access privileges when users are no longer needed or authorized.The shortcomings of existing systems in ensuring efficient, streamlined access revocation and tamper-proof protection of access control policies underscore the need for innovative solutions.In this paper, we have introduced the novel Blockchain Attribute-Based Secure Data Management Model (BAB-SDMM).Our model is the first to integrate attribute-based encryption (ABE), Attribute-Based Access Control (ABAC), and blockchain to achieve multiple security features as well as provide partial and complete revocation at the same time.The experimental results and analysis, performed using the Ethereum blockchain network, demonstrated the enhanced performance of the proposed BAB-SDMM compared to existing research works.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".