Dual-anonymous reward distribution for mobile crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing enables individuals to collect data from social events and phenomena for performing tasks released by customers using their mobile devices. It removes the necessity of sensors deployment and hence supports large-scale sensing applications efficiently. Nevertheless, incentive and privacy remain as the major obstacles that need additional attention. If privacy is not presered or no benefit obtains, no mobile user prefers to participate in crowdsensing activities. In this paper, we propose DARD, a dual-anonymous reward distribution scheme to achieve the incentive for mobile users and privacy protection for both customers and mobile users in mobile crowdsensing. Specifically, we design a reward sharing incentive mechanism to encourage mobile users to participate in tasks and employ randomizable techniques to protect the identities of customers and mobile users during reward claim, distribution and deposit. Our analysis further shows that DARD achieves reward balance and cheater detection with low computational and communication overhead.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".