Verifiable Outsourced Database Model: A Game-Theoretic \nApproach
Bibliographic record
Abstract
In the verifiable database (VDB) model, a computationally weak client (database owner) delegates \nhis database management to a database service provider on the cloud, which is considered \nuntrusted third party, while users can query the data and verify the integrity of query results. Since \nthe process can be computationally costly and has a limited support for sophisticated query types \nsuch as aggregated queries, we propose in this research a framework that helps bridge the gap between \nsecurity and practicality. The proposed framework remodels the verifiable database problem \nusing Stackelberg security game. In the new model, the database owner creates and uploads to \nthe database service provider the database and its authentication structure (AS). Next, the game is \nplayed between the defender (verifier), who is a trusted party to the database owner and runs scheduled \nrandomized verifications using Stackelberg mixed strategy, and the database service provider. \nThe idea is to randomize the verification schedule in an optimized way that grants the optimal payoff \nfor the verifier while making it extremely hard for the database service provider or any attacker \nto figure out which part of the database is being verified next. \nWe have implemented and compared the proposed model performance with a uniform randomization \nmodel. Simulation results show that the proposed model outperforms the uniform randomization \nmodel. Furthermore, we have evaluated the efficiency of the proposed model against \ndifferent cost metrics.
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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 | 0.004 |
| 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 | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".