SDIVIP<sup>2</sup>: shared data integrity verification with identity privacy preserving in mobile clouds
Bibliographic record
Abstract
Summary Mobile networks integrate cloud computing to impair the weaknesses of the mobile terminals. With mobile cloud storage, mobile users can fully enjoy the advantages from both mobile networks and cloud storage. However, a major concern of mobile users is how to guarantee the integrity of their outsourced data. Taking into account the mobility of mobile devices, in this paper, we propose a shared data integrity verification protocol with identity privacy preserving, named SDIVIP2, for mobile cloud storage. In the construction of SDIVIP2, the dynamic group key agreement technique is employed for key sharing among a group of mobile users and the proxy re‐signature mechanism is utilized to update tags efficiently when users in the group change. In this new protocol, a third party auditor is able to verify the correctness of cloud data without the knowledge of mobile users' identities during the data integrity checking process. Performance analysis demonstrates that SDIVIP2outperforms the existing schemes in the sense that it can significantly enhance the efficiency of mobile users' joining and leaving a group. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".