Secure and Efficient Probabilistic Skyline Computation for Worker Selection in MCS
Bibliographic record
Abstract
The rapid advance of the Internet of Things (IoT) has enabled a new paradigm of the sensing network, i.e., mobile crowdsensing (MCS). Primarily, in MCS systems, a crowd of participating mobile users, namely, workers, are allocated by the MCS platforms to outsource their sensory data for specific tasks. Obviously, the reliability of workers and the trustability of their sensing data play significant roles in the service quality, thus the worker selection becomes crucial for the success of MCS applications. However, due to either a large number of candidates or their dynamic natures, selecting reliable workers poses big challenges to the MCS platform. Evidently, workers' reputation-based characteristics, such as trustability and credibility, are also pivotal for the worker selection in MCS, but they were often neglected in previous literature. In this article, aiming at addressing the above challenges, we propose a new privacy-preserving worker selection scheme based on the probabilistic skyline computation technique. Specifically, our proposed scheme is characterized by: 1) assigning a trustability score to each worker based on his/her past performance without revealing his/her sensitive information and 2) efficiently selecting a subset of reliable workers for a particular task. Detailed security analysis shows that our proposed scheme can preserve workers' privacy. In addition, performance evaluations via extensive simulations are conducted, and the results also demonstrate its effectiveness and efficiency for reliable worker selection in MCS applications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".