Bibliographic record
Abstract
By leveraging and modifying ciphertext-policy attribute based encryption (CP-ABE) and OAuth, we propose a new authorization scheme, called fuzzy authorization, to facilitate an application registered with one cloud party to access data residing in another cloud party. The new proposed scheme enables the fuzziness of authorization to enhance the scalability and flexibility of file sharing by taking advantage of the one-to-one correspondence between linear secret-sharing scheme (LSSS) and generalized Reed Solomon (GRS) code. Furthermore, by conducting attribute distance checking and distance adjustment, operations like sending attribute sets and satisfying an access tree are eliminated. In addition, the automatic revocation is realized with update of TimeSlot attribute when data owner modifies the data. The security of the fuzzy authorization is proved under the d-BDHE assumption. In order to measure and estimate the performance of our scheme, we have implemented the protocol flow of fuzzy authorization with OMNET++ 4.2.2 and realized the cryptographic part with pairing-based cryptography (PBC) library. Experimental results show that fuzzy authorization can achieve fuzziness of authorization among heterogeneous clouds with security and efficiency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".