SkyCloaking: A UAV-Assisted Privacy-Preserving Strategy for Location-Based Service Users
Bibliographic record
Abstract
Location-based services (LBSs) play a vital role in many Internet applications. Privacy is a mandatory aspect of these tasks, including protecting the user's sensitive information from malicious entities. Location privacy-preserving mechanisms (LPPMs) were designed to ensure privacy for LBS users, and several strategies have emerged, such as cloaking mechanisms. Likewise, several attacks appeared to threaten the user's privacy, being based on ground-related aspects. Therefore, we must investigate new strategies to enhance privacy protection mechanisms. Unmanned Aerial Vehicles (UAVs) can provide assisted coverage to ground users in different tasks, including the support of LPPMs. However, the existing strategies rely on some unfeasible premises, and this collaboration needs to be adequately explored. Therefore, in this study, we propose Sky Cloaking, which promotes the opportunistic connection between ground users and UAVs in such a way the UAVs manage the user's query, creating a cloaking region and hampering the success of an attacker. Through a comprehensive evaluation, we demonstrated that SkyCloaking can ensure high levels of location privacy to LBS users with a slight impact on the communication channel, overcoming existing strategies. In the best scenarios, SkyCloaking protected more than 80% of the user trajectory, mitigating the exploitation of users' sensitive information.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".