Wireless Location Privacy Protection in Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
Advances in mobile networks and positioning technologies have made location information a valuable asset in vehicular ad-hoc networks (VANETs). However, the availability of such information must be weighted against the potential for abuse. In this paper, we investigate the problem of alleviating unauthorized tracking of target vehicles by adversaries in VANETs. We propose a vehicle density-based location privacy (DLP) scheme which can provide location privacy by utilizing the neighboring vehicle density as a threshold to change the pseudonyms. We derive the delay distribution and the average total delay of a vehicle within a density zone. Given the delay information, an adversary may still be available to track the target vehicle by a selection rule. We investigate the effectiveness of DLP based on extensive simulation study. Simulation results show that the probability of successful location tracking of a target vehicle by an adversary is inversely proportional to both the traffic arrival rate and the variance of vehicles' speed. Our proposed DLP scheme also has a better performance than both Mix-Zone scheme and AMOEBA with random silent period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".