Protocols Design and Area Division for Privacy-Preserving Delay-Aware Authentication in Vehicular Networks
Bibliographic record
Abstract
The problem of security and privacy in vehicular networks is a vital issue, and it attracts increasing attention to address the security vulnerability of vehicular networks. Authentication solutions are introduced for vehicular networks to ensure that network access is only given to authorized users. Among authentication solutions for vehicular networks, group signature not only offers authentication services, but also provides conditional privacy preservation. However, the current group signature approach for authentication in vehicular networks exhibits time-consuming signature verification and poor scalability. To overcome these shortcomings, we propose a flexible and efficient delay-aware authentication scheme (FEDAS) by utilizing edge computing paradigm. In the proposed architecture, we design the authentication group maintaining mechanism and develop the collaborative CRL management method. Moreover, we propose transition zone to solve the reliable authentication problem in border area of the group. To implement the proposed architecture, we propose a model for calculating the length of local CRL, which establishes the relationship between the size of a sub-area and the length of local CRL. And we also design a method for area division based on the length of local CRL, which provides division principle for our authentication scheme. We conduct extensive simulations to verify the effectiveness of our proposed scheme.
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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 | 0.003 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".