A secure business framework for file purchasing in vehicular networks
Bibliographic record
Abstract
Abstract Vehicular ad hoc networks (VANETs) are gaining growing interest from both industry and academia. The primitive objective of VANETs is to enhance the road safety for public transportation systems through dedicated short range communications (DSRC) protocol. In addition to the powerful radios and abundant spectrums, DSRC also paves the way for VANETs to support numerous emerging Internet‐related applications. In this paper, we introduce a promising commercial application which allows each vehicle to purchase file/data through a roadside unit (RSU). Due to the high mobility of vehicles, the contact period between an RSU and a vehicle could be insufficient to download the complete file. Thus, in the proposed file purchasing systems, once a vehicle in the process of downloading a file leaves the transmission range of the RSU, its neighboring vehicles with a piece of the file can cooperatively help to complete the file transfer via vehicle‐to‐vehicle (V2V) communications. Such a commercial file purchasing system can obviously initiate a new application scenario; however, it cannot be put into practice unless the security issues, such as the user privacy, incentives for inter‐vehicle cooperation, and the copyright protection for∼the file content, are well addressed. In order to deal with these security issues, we develop a secure framework for the file purchasing system in VANETs. Performance evaluation will be conducted to verify the proposed scheme. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".