A Conditional Privacy-Preserving Protocol for Cross-Domain Communications in VANET
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) empower vehicles equipped with onboard units to exchange traffic-related messages, enhancing vehicle navigation safety and efficiency. Providing secure privacy-preserving authentication schemes for VANETs is indispensable. It ensures that only legitimate vehicles can communicate, preventing external adversaries from injecting falsifiable information that could mislead vehicles, cause accidents, or disrupt traffic flow. Simultaneously, the privacy-preserving features prevent curious adversaries from compromising vehicle privacy and tracking users. Secure centralized vehicular communication protocols, where a single entity issues certificates for all vehicles, face challenges in enabling cross-domain communications. Adoption of such centralized protocols necessitates that vehicles within each domain possess their certificate authority, restricting cross-domain communication due to inherent distrust in the certificate authorities of other domains. In this paper, we propose a Conditional Privacy-preserving Message Authentication protocol for VANET Emergency message exchange (CP-MAVE), designed to ensure message authentication, integrity, and anonymity of vehicles across different domains. In the event of misbehavior, distributed key generation centers collaborate to trace back the identity of the vehicle. To evaluate the security of our protocol, we formally prove the existential unforgeability of CP-MAVE against chosen message attacks based on the intractability of the elliptic curve discrete logarithm problem. Additionally, we demonstrate that CP-MAVE achieves message authentication, conditional privacy preservation, and resilience against replay and modification attacks. Moreover, we model and analayze CP-MAVE using the Tamarin prover and show that CP-MAVE maintains the secrecy and the message authentication of the vehicle traffic messages. Furthermore, we evaluate CP-MAVE’s performance regarding communication overhead and computation complexity. On a Raspberry Pi 4 Model B/8GB, equipped with a 1.5 GHz 64-bit Quad-core ARM Cortex-A72 processor, CP-MAVE requires a 304-byte communication overhead and 9.4897 msec as cryptographic operation overhead. Finally, to simulate the flow of messages between entities in our protocol, we implement CP-MAVE using socket programming, resulting in an end-to-end delay of 111.05 msec.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".