RIN: A dynamic pseudonym change system for privacy in VANET
Bibliographic record
Abstract
Summary We propose in this paper a security protocol based on a dynamic change of pseudonym for privacy and anonymity in Vehicular Ad hoc NETworks (VANET). Our proposal ensures privacy for the driver and his vehicle whether he is the transmitter or receiver of the message. By handling all possible cases of changes in vehicles behavior during traffic, we ensure a safe and secure traffic management. We built the architecture of our solution on three essential entities designed for VANET: a trusted authority, a Road Side Unit, and vehicles. Our pseudonym change system ensures anonymity at any time, anywhere, with any speed of a vehicle, in all directions of a vehicle, in any environment (highway or urban), with any traffic density, independently of neighboring vehicles, and changes in vehicles behavior. In three steps, anonymity, integrity, and confidentiality are guaranteed by our RIN (Real Initial New) protocol. Our protocol blocks attacks as DOS and Sybil. Results obtained by the simulations show the efficiency of our solution. Compared with other relevant approaches from the literature, the results confirm the credibility of our protocol regarding to the privacy assurance in VANET.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".