EPTS: Efficient and Privacy-Preserving Outsourced Task Scheduling in Vehicular Crowdsourcing
Bibliographic record
Abstract
The flourishing of intelligent connected vehicles (ICVs) has fostered the emergence of vehicular crowdsourcing (VCS) applications, in which ICVs function as workers to execute diverse spatio-temporal critical tasks. As a vital service of VCS, task scheduling aims to assign tasks to the most suitable workers. To cope with the escalation of service scale, the service provider tends to outsource the service to powerful cloud servers, which however triggers the privacy concerns of workers, task owners, and the service provider. Previously reported privacy-preserving task allocation schemes can mainly be divided into single-attribute-aware and multiattribute-aware schemes. Nevertheless, the former suffers from practicality issues, while the latter either fails to achieve single-dimensional privacy and access pattern privacy or introduces substantial computational costs. To tackle the above challenges, we propose an efficient privacy-preserving outsourced task scheduling scheme (EPTS) for VCS, in which two cloud servers can cooperate to efficiently and securely conduct multiattribute-aware task scheduling. To this end, we devise five lightweight secure two-party protocols under the additive secret sharing (ASS) setting, which enable cloud servers to obliviously filter workers that meet multiple constraints and traverse the candidate worker set to obtain the optimal worker without revealing the input and output. Rigorous security analysis proves that our EPTS scheme effectively preserves user privacy, single-dimensional privacy, and access pattern privacy. Extensive experimental results validate its superior efficiency 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".