Achieving Efficient and Privacy-Preserving Worker Selection With Arbitrary Spatial Ranges for Vehicular Crowdsensing
Bibliographic record
Abstract
The proliferation of intelligent connected vehicles (ICVs) has catalyzed the emergence of vehicular crowdsensing (VCS) applications, wherein sensing tasks are assigned to ICVs with abundant sensing resources and high mobility. To select workers whose future trajectories have sufficient spatio-temporal similarity with the target sensing area, workers unavoidably need to upload their trajectories to the VCS platform that is not fully trusted, thereby triggering location privacy concerns. Recently, numerous privacy-preserving worker selection schemes have been put forth. Nevertheless, they either fail to enable flexible arbitrary query ranges or incur substantial communication and computation costs, which severely limits their suitability for VCS applications. To tackle the above two issues simultaneously, we propose a novel efficient and privacy-preserving VCS worker selection scheme that supports flexible arbitrary spatial ranges. By utilizing the Bloom filter technique and lightweight cryptographic tools, our proposed scheme allows the VCS platform to efficiently collaborate with the fog server to compute the spatio-temporal similarity without leaking location-derived Bloom filters. Rigid security analysis shows that our scheme effectively preserves the location privacy of both workers and the query user. Extensive experiments are conducted and the results demonstrate that our scheme is significantly more efficient in both communication and computation compared with the state-of-the-art scheme.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".