A Topological Dummy-based Location Privacy Protection Mechanism for the Internet of Drones
Bibliographic record
Abstract
The recent advancement of drone technologies and communication protocols allows us to envision a robust and dynamic mobile vehicular network paradigm called the Internet of Drones (IoD). In this environment, drones will perform several location-based services (LBSs) for users, awakening the interest of malicious entities whose intention is to hamper the service. Hence, drones need high protection regarding their localization in LBSs. However, there is a lack of Location Privacy Protection Mechanisms (LPPMs) for an IoD scenario. Dummy-based LPPMs provide proper location privacy in traditional mobile networks, mainly for sparse configurations. The design of this mechanism for IoD can overcome this deficiency. This study proposes a novel dummy-based LPPM for the IoD, called TDG, that focuses on the IoD topology characteristics regardless of near drones being the first approach presented in this context. Through extensive experiments, we show that TDG can provide proper location privacy for drones in sparse configurations, reducing the use of the wireless communication channel. TDG can protect the real drone’s trajectory up to more than 90% of the time, leaking less than 25% of the drone’s real coordinates.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".