Achieving Privacy-Preserving Location Management in LEO-Satellite Integrated Vehicular Network With Dense Ground Stations
Bibliographic record
Abstract
Low Earth orbit (LEO) satellite constellations support intelligent driving applications in areas without terrestrial network coverage. As the LEO-satellite integrated vehicular network experiences dual mobility of satellites and vehicles, the mainstream IP-based mobility management protocols may not adapt to the dynamic network topology and violate location privacy. Given the above challenges, we propose a secure and privacy-preserving distributed location management (DMM) scheme in a LEO-satellite integrated vehicular network with dense ground stations. The proposed scheme achieves the privacy-preserving location update through a conditional privacy preservation protocol, which guarantees secure data delivery when the binding ground station changes before the periodic pseudonym update. Meanwhile, the proposed scheme achieves the privacy-preserving and multi-level data delivery with batch authentication. As our scheme is the first concerning location privacy in an LEO satellite constellation, we compare it with two competing schemes: the first without a Merkle hash tree and the second without a cuckoo filter. Simulation results show that ours outperforms the two competing schemes regarding computation costs and communication overhead. To balance the trade-off between privacy and complexity, we also formulate an objective function concerning the pseudonym update period and derive its optimal solution.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".