A Context-Aware Privacy Scheme for Crisis Situations
Bibliographic record
Abstract
Participatory sensing allows individuals and groups to contribute to an application using their handheld sensor devices. Data collected from participants including their location, time, contacts, etc. are vital to the accuracy of the application but are considered private to the participants. The design of a successful participatory sensing application must consider the challenge of the accuracy-privacy trade-off. In more critical situations when a crisis occurs, however, the accuracy-privacy trade-off becomes more complex. When a participant is at risk, data accuracy becomes more important than participant's privacy. In this paper, we propose a Context-Aware Privacy (CAP) scheme. CAP aims to provide privacy- preserved data to authorized recipients based on the status of participants. Depending on the recipient category, their role and policies enforced, a different level of participants' private data may be received. Experimental results show that the CAP scheme achieves a high level of privacy protection in safe areas. In risk areas/situations the scheme achieves a higher level of data accuracy than existing privacy schemes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".