Special Issue on “Security and Privacy Preservation in Vehicular Communications” Wiley's Security and Communication Networks Journal
Bibliographic record
Abstract
Abstract It has been witnessed that the car manufacturers and telecommunication industries gear up to equip each car with the latest wireless communication technologies, most notably the short‐range communication systems and/or networks (vehicle‐vehicle or vehicle‐roadside) based on IEEE 802.11p. The short‐range vehicular communication technologies are expected to evolve into VANETs (Vehicular Ad‐hoc NETworks), which will be supporting various safety and commercial applications that significantly improve the driving experiences and safety. The merits of launching VANETs are obvious; however, it comes with a set of challenges, especially in the aspects of security and privacy preservation, in which any malicious behavior of users, such as a modification and replay attack with respect to the disseminated messages, could be fatal to the other users. In addition, the issues on VANET security become more challenging due to the unique features of such network scenarios, including high‐speed mobility and large amount of network entities (i.e., the vehicles). Furthermore, conditional privacy preservation must be achieved in a sense that the user related privacy information, including the driver's name, the license plate, speed, position, and traveling routes along with their relationships, has to be protected; while the authorities should be able to reveal the identities of message senders in the event of a traffic dispute, such as a crime/car accident scene investigation. This special issue aims to address the aforementioned issues by collecting six technical papers through a peer‐review process, hoping to contribute to the state‐of‐the‐art progress of secure and privacy preserving vehicular communications. 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.029 |
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".