A Privacy-Preserving and Verifiable Querying Scheme in Vehicular Fog Data Dissemination
Bibliographic record
Abstract
Vehicular fog has attracted considerable attention recently, as the densely deployed fog devices are in proximity to vehicular end-users, and they are particularly suitable for the latency-sensitive and location-aware vehicular services. In this paper, we propose a secure querying scheme in vehicular fog data dissemination, in which the roadside units (RSUs) act as fog storage devices to cache data at network edge and disseminate data upon querying. To disrupt the association between a specific data request and its origin vehicle, the proposed scheme exploits an invertible matrix to structure multiple data requests from different vehicles, and aggregates the ciphertexts of data requests at the RSU side with the homomorphic Paillier cryptosystem. Meanwhile, given the invertible matrix and decryption result, the RSU can recover each individual data request without identifying its origin vehicle. In addition, the RSU can verify the correctness of the recovered data requests with an identity-based batch verification scheme. Through security analysis, we demonstrate that the proposed scheme can achieve the security goals of unlinkability, confidentiality, and verifiability. Performance evaluations are also conducted, in which the obtained results show that the proposed scheme can be adaptive to the fluctuating number of the data querying vehicles, and significantly reduce the computation complexity and communication overhead.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".