SaaS Authentication Middleware for Mobile Consumers of IaaS Cloud
Bibliographic record
Abstract
The mobile terrain is rapidly establishing itself as the reliable node for accessing cloud hosted data. Today, commodity cloud providers especially from the Infrastructure-as-a-Service (IaaS) cloud expose their service APIs which facilitates the "app-ification" of enterprise workflows on mobile devices. However, these IaaS providers require the customer (i.e., the data consumer) to submit multiple security credentials which are computation intensive for the purposes of authentication and authorization. As a result, the authentication process introduces undesired delays in a mobile network when consuming enterprise data due to the increasing computational demand and the voluminous HTTP header that is transported across the wireless bandwidth.This paper introduces an application called MiLAMob that is a middleware-layer that handles the authentication process on behalf of the consumer devices in real time and with minimal HTTP traffic. The middleware currently supports mobile consumption of data on IaaS clouds such as Amazon S3, Dropbox, and MEGA. Further, the middleware employs the OAuth 2.0 technique (E.g. Facebook, Google+, and Personal Login) to identify the mobile end-user and uses security tokens to handle the tedious authentication with the IaaS cloud. Also, the deployment of the middleware enforces additional data protection because the security credentials and the IaaS abstractions are shielded from the mobile application domain and the end users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".