Game Theoretical Analysis on Acceptance of a Cloud Data Access Control System Based on Reputation
Bibliographic record
Abstract
With the rapid development of the Internet, cloud storage has penetrated into every aspect of human society. However, cloud data disclosure happens more and more frequently, which makes cloud data security and privacy protection impact wide adoption of cloud storage. Control cloud data access based on reputation by introducing a Reputation Center (RC) was proposed and demonstrated to secure cloud data effectively in [9] . But the acceptance of such a system by cloud users and Cloud Service Providers (CSPs) is crucial for its practical deployment and final success. In this paper, we investigate the acceptance of a cloud data access control system based on reputation using Game Theory. Due to the existence of dishonest CSPs, there exists a social reputation dilemma among CSPs, which seriously impedes the popularity of cloud storage. To encourage users to use cloud storage and suppress collusion between CSPs and data requesters, a repeated public-goods game is built up by applying a compensation mechanism to improve the utilities of cloud users and a punishment mechanism based on reputation to incent honest behaviors. Theoretical analysis and simulation results show the effectiveness of the compensation and punishment mechanisms to increase cloud storage rate and restrain dishonest system entities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".