OAuth and ABE based authorization in semi-trusted cloud computing
Bibliographic record
Abstract
In cloud computing, inter-operations between data-storage and web-application providers can protect users from locking their data and applications into a single cloud provider. Currently, web-based access control standards are applicable only when data owners and cloud service providers are in the same trusted domain. Unfortunately, this condition cannot be satisfied in untrusted clouds, where cloud providers may access sensitive information without authorization. Most previous studies require end-user certificates or specific APIs and depart from existing standards. In this paper, we propose a new authorization scheme (AAuth) that builds on the OAuth standard by leveraging ciphertext-policy attribute based encryption and an ElGamal-like mask over the HTTP protocol. Our scheme provides end-to-end encryption and ABE-based tokens to enable authorization by both authorities and owners and to move policy enforcement from clouds to destinations. With our user-centric approach, owners can take control of their data when it rests in semi-untrusted cloud storage. Moreover, with most cryptographic functions delegated from owners to authorities, owners can gain computation power from clouds. Security analysis shows that our scheme maintains the same security level as the original encryption scheme and protects users from exposing their credential to application providers. In our extensive simulation, AAuth's greater overhead was balanced by greater security than OAuth's. Furthermore, our scheme works seamlessly with storage providers by retaining the providers' APIs in the usual way.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".