AirQ: A Privacy-Preserving Truth Discovery Framework for Vehicular Air Quality Monitoring
Bibliographic record
Abstract
Air pollution has become an important health concern. The recent developments of vehicular networks and crowdsensing systems make it possible to monitor fine-grained air quality with vehicles and road-side units. On account of the different precisions of onboard sensors and malicious behaviors of participants, sensory data usually vary in quality. Thus, truth discovery has been a crucial task which targets at better utilizing the data. However, in urban cities, there is a significant difference in traffic volumes of streets or blocks, which leads to a data sparsity problem for truth discovery. To tackle the challenge, we present a truth discovery algorithm incorporating spatial and temporal correlations. Besides, to protect the privacy of participating vehicles, we develop the algorithm into a privacy-preserving truth discovery framework by adopting the technique of masking. The proposed framework is lightweight than the existing cryptography-based methods. Simulations are conducted to show that the proposed framework has a good performance. Although the framework is presented for air quality monitoring, we fully discuss the possible applications and extensions.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".