Toward Privacy-Preserving Spatial Crowdsourcing: From Offline to Online
Bibliographic record
Abstract
Recent years have seen Spatial Crowdsourcing (SC) becoming a promising paradigm of collecting large amounts of location-aware data for various network applications. Its strengths lie in leveraging the power of the crowd to gather data effectively at a low cost. However, during the interaction process between SC participants (e.g., workers and task requestors) and the SC platform, participants’ sensitive information is inevitably exposed, raising severe privacy concerns. Existing studies addressing these privacy concerns have mostly focused on offline SC settings, where the participants’ information is known and accessible in advance. Yet real-world SC participants can be highly dynamic with uncertain behaviors; hence, in practice, their information is largely unknown a priori. In this article, we closely examine the privacy challenges as well as the trade-off between privacy and efficiency in online SC settings. We develop a comprehensive online privacy-preserving SC framework and analyze the critical implementation issues.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".