Blockchain-Based Pseudonym Management Scheme for Vehicular Communication
Bibliographic record
Abstract
A vehicular ad hoc network (VANET) consists of vehicles, roadside units, and other infrastructures that communicate with each other with the goal of improving road safety, reducing accidents, and alleviating traffic congestion. For safe and secure operation of critical applications in VANET, it is essential to ensure that only authenticated vehicles can participate in the network. Another important requirement for VANET communication is that the privacy of vehicles and their users must be protected. Privacy can be improved by using pseudonyms instead of actual vehicle identities during communication. However, it is also necessary to ensure that these pseudonyms can be linked to the real vehicle identities if needed, in order to maintain accountability. In this paper, we propose a new blockchain-based decentralized pseudonym management scheme for VANET. This allows the vehicles to maintain conditional anonymity in the network. The blockchain is used to maintain a record of each vehicle and all of its pseudo-IDs. The information in the blockchain can only be accessed by authorized entities and is not available to all vehicles. The proposed distributed framework maintains an immutable record of the vehicle data, which is not vulnerable to a single point of failure. We compared the performance of the proposed approach with a traditional PKI scheme and shown that it significantly reduces the authentication delay.
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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 | 0.003 |
| 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 | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".