BAIV: An Efficient Blockchain-Based Anonymous Authentication and Integrity Preservation Scheme for Secure Communication in VANETs
Bibliographic record
Abstract
Recent development in intelligent transport systems (ITS) has led to the improvement of driving experience in vehicular ad-hoc network (VANET) systems. Providing a low computational cost with high serving capability, however, is a critical phenomenon in the current VANET system. In the existing scenario, when the authenticated vehicle user moves from one roadside unit (RSU) to another RSU region, re-authentication of the vehicle user is required by the current RSU, which increases the computational complexity. To overcome the above-mentioned challenge, a blockchain-based authentication protocol is developed in this work. In this suggested process, blockchain is integrated with VANET, which enables the authentication of the vehicle user without the involvement of a trusted authority. Moreover, the integrity of the message and privacy of vehicle users are preserved in the blockchain network. Even though many blockchain-based schemes have been proposed recently, the existing schemes were not focused on conditional anonymity. However, in our proposed scheme, conditional privacy is introduced to revoke the malicious vehicles in the case of disputes and to avoid further damage to the VANET system. As a result, the proposed scheme provides an efficient mechanism for anonymous authentication, privacy, and integrity preservation with conditional tracking. Finally, the defense against different security threats is explained in the security analysis section, and the performance investigation section shows the competence and efficacy of our method with similar related methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".