Motivation for Protecting Selfish Vehicles' Location Privacy in Vehicular Networks
Bibliographic record
Abstract
Location privacy is an important issue in vehicular networks since knowledge of a vehicle's location can result in leakage of sensitive information. A way to protect vehicles' location privacy is to have them change their pseudonyms in predetermined regions known as mix zones. However, selfish vehicles may not change their pseudonyms because of limited resources (such as pseudonyms and bandwidth). This could jeopardize the location privacy of those vehicles that are in need of changing their pseudonyms. To encourage vehicles to cooperate in changing their pseudonyms, we propose a method called Motivation for Protecting Selfish Vehicles' Location Privacy (MPSVLP). In MPSVLP, vehicles can form a mix zone dynamically when their pseudonyms are close to expiration and can also earn reputation “credit” by implementing a pseudonym change. The simulations show that MPSVLP motivates more selfish vehicles to cooperate with each other while maintaining a high level of location privacy. In addition, MPSVLP can reduce communication overhead in comparison with the existing methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".