ASIC: Aggregate Signatures and Certificates Verification Scheme for Vehicular Networks
Bibliographic record
Abstract
To achieve high safety levels in vehicular ad-hoc networks (VANETs), the Dedicated Short Range Communication (DSRC) implies each vehicle to periodically broadcast a safety beacon message. Many safety-related applications in VANET are based on the safety beacon messages. To ensure secure communications, the message authentication and integrity must be verified by respectively verifying the public key certificate and the digital signature of the sender. Since each vehicle could receive a large number of messages from the neighboring vehicles, one of the inevitable VANET challenges is the ability for each vehicle to verify a large number of messages in a timely manner. In this paper, we propose an aggregate signatures and certificates (ASIC) verification scheme enabling each vehicle to simultaneously verify not only the signatures but also the certificates of the senders. ASIC significantly increases the vehicle capability to verify a large number of signatures and certificates in a timely manner. Performance evaluation demonstrates that ASIC is reliable, efficient, and scalable.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".