Evaluating the effectiveness of pseudonym changing strategies for location privacy in vehicular ad‐hoc network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a vehicular ad‐hoc network, vehicles periodically broadcast safety messages to neighboring vehicles containing information about their identity, location, speed, and other relevant information. The time‐critical nature of these messages means they are typically sent without encryption, which can compromise the privacy of the vehicle/driver. The use of pseudonyms, rather than the actual identity, has been suggested as a way to implement location privacy for drivers. However, such pseudonyms need to change frequently in order to prevent vehicle tracking. A pseudonym changing strategy (PCS) determines how frequently and under which conditions a vehicle should change its pseudonym. Many different PCS have been proposed in the literature over the last decade. However, there is a lack of a systematic analysis of the performance of these techniques, using a set of well‐defined privacy metrics. In this article, we provide a thorough comparison of four main types of PCS, in terms of standard privacy metrics, and identify the advantages and limitations of each approach under different traffic conditions and attacker capabilities. The ultimate aim of this study is to help in the determination of the best PCS to use in different situations, to maximize the location privacy.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it