Special Issue on “Security and Privacy Preservation in Vehicular Communications” Wiley's Security and Communication Networks Journal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it