Server-Assisted Data Sharing System Supporting Conjunctive Keyword Search for Vehicular Social Networks
Bibliographic record
Abstract
Vehicular social networks (VSNs), as the convergence of social networks and vehicular ad hoc networks, have brought many useful services to vehicle communication by collecting and sharing data between vehicles. In order to efficiently share data and satisfy the growing requirement of privacy protection, data owners typically encrypt and outsource the data to the cloud. Nevertheless, encryption undoubtedly reduces the availability of shared data, e.g., keyword search. Although a number of schemes supporting keyword search of shared data have been put forward, they still have issues with respect to security, functionality, and efficiency. In this paper, a server-assisted data sharing (SADS) system with support for conjunctive keyword search is presented. Specifically, to resist online keyword guessing attack, we devise an advanced keyword derivation mechanism to derive the keyword set, in which the conception of verifiable parallel oblivious unpredictable function is proposed to check whether the assisted server honestly responds to the derived keyword request. Moreover, the computation and communication costs of keyword trapdoor in SADS are constant. Concurrently, SADS achieves the anonymous data sharing and traceability of malicious vehicle data owner. The security of SADS is formally proved and analyzed. Performance evaluation also shows that our system is efficient and practical.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".