Secure Identity Management Framework for Vehicular Ad-hoc Network using Blockchain
Bibliographic record
Abstract
Vehicular Ad Hoc Network (VANET) is a mobile network formed by vehicles, road side units, and other in-frastructures that enable communication between the nodes to improve road safety and traffic control. While this technology promises great benefits to drivers, there are many security and privacy concerns that must be addressed before it can be fully adopted. It is essential to ensure that vehicles participating in the network are authenticated and held accountable in case of misbehaviour. On the other hand, there should be adequate mechanisms for preserving the privacy of vehicles and drivers, so they are protected against unauthorized tracking and release of private information. Many current VANET technologies also depend on a central trusted authority that becomes a single point of failure for the network. In this paper, we propose a new blockchain based decentralized authentication approach for VANET. In this scheme vehicles maintain conditional anonymity in the network and their real identities can only be revealed to authorized entities. Using the blockchain technology, we create a distributed framework and maintain an immutable record of the data, strengthening the integrity of the system. We use the Hyperledger Fabric, a permissioned blockchain technology, to implement our approach and compare its performance to the traditional PKI based method for VANET authentication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".