A Context Aware and Traffic Adaptive Privacy Scheme in VANETs
Bibliographic record
Abstract
Preserving privacy in VANETs is a significant challenge for users and public acceptance of VANETs. The use of a pseudonym is a common technique for enhancing the user's privacy in VANETs. Several Pseudonym Changing Schemes (PCS) for user's privacy in VANETs have been proposed. The highly dynamic topology of the vehicular network can impact the way the pseudonymous identifiers are changed. To make these changes inconspicuous, we introduce the Context-Aware and Traffic Adaptive privacy scheme, which takes into account the rapidly changing traffic condition. In this paper, we propose a new PCS that aims to benefit the most from the context of the vehicle and traffic patterns to leverage a suitable situation for changing pseudonyms that increases anonymity. The vehicles change the pseudonym simultaneously in a region to increase privacy by maximizing the anonymity set. The proposed approach is evaluated in the presence of an adversary actor who could engineer privacy attacks against any given PCS.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".