Transparent and Accountable Vehicular Local Advertising With Practical Blockchain Designs
Bibliographic record
Abstract
Vehicular local advertising enables roadside retailers to provide timely and location-aware advertisements to on-road vehicular users to attract more in-store visits. However, the prevalence of the vehicular local advertising has raised increasing transparency and accountability concerns on the spatial keyword query process, such as why a vehicular user receives advertisements from specific spatial entities as well as the timely detection of the advertising misbehavior. In this paper, we develop a transparent and accountable vehicular local advertising system by utilizing the tamper-proof and open nature of the blockchain technology. Considering the large scale of the spatial-keyword database and the expensive computation and storage cost on the blockchain, we introduce two design strategies, digest-and-verify and divide-then-assemble. In specific, a large-scale spatial keyword database is digested and stored on the blockchain. The spatial keyword query is then conducted with modular executions of two off-chain spatial keyword query functions, the results of which are efficiently assembled and verified in an advertising smart contract. By doing so, expensive on-chain computation and storage overheads are significantly reduced at a cost of acceptable off-chain overheads. We define the security requirements of the vehicular local advertising system as Auditing Security and achieve the notion with the security analysis. We also conduct real-world experiments to show the efficiency of the blockchain-based vehicular local advertising system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".