Protection of Geoprivacy and Accuracy of Spatial Information: How Effective Are Geographical Masks?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial analysis and mapping of georeferenced, individual-level data can help identify important geographical patterns or lead to knowledge significant for dealing with specific social issues in a particular area. However, given the need to protect personal privacy when using geospatial data, the possibility for undertaking geographical analysis on certain types of individual-level data is becoming increasingly circumscribed. This article addresses the need to protect geoprivacy while making georeferenced, individual-level data available in such a way that analytical results are not significantly affected. The effectiveness of three geographical masks with different perturbation radii (r) is examined using a data set for lung-cancer deaths in Franklin County, Ohio, in 1999. The findings reveal a rather consistent trade-off between data confidentiality and accuracy of analytical results. There seems to be a threshold r-value at which the results of analyses on masked data become substantially different from the original results. An r that produces an area about the average size of the study-area census-block groups achieves a desirable optimum trade-off between privacy protection and accuracy of results. The study shows that implementing appropriate geographical masks may help data managers or researchers establish the desirable trade-off, in a particular context, between privacy protection and accuracy of geographic information.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 it