Proof of concept of a security based on lifetime of communication's pseudonyms for the VANETs
Bibliographic record
Abstract
To make Vehicular Ad Hoc Network (VANET) applications useful to the users, the security problem must be solved. Recent researches have suggested the use of a set of anonymous keys certified by the issuing CA (Central authority) to preserve privacy, authentication, and confidentiality of the communicating entities. But how to determine the right time for the vehicles to exchange their communication pseudonyms and what would be the impacts on the network resources such as time processing and memory. In this paper, we propose a protocol that preserves authentication, non repudiation, and location privacy and helps vehicles to exchange their pseudonyms at roughly the same time. It is based on calculating the Euclidean distance and the average of the speed permitted on the path to evaluate the lifetime of the communication's pseudonyms. The exchange of the information is based on asymmetric and symmetric cryptography scheme and it uses hash function. The protocol permits to determine the expiration time of pseudonyms and how to make the distribution of all the road side units along the road in order to establish a good communication between them and the vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".