MaskMy.XYZ: An easy‐to‐use tool for protecting geoprivacy using geographic masks
Bibliographic record
Abstract
Abstract Geographic masks are techniques used to protect privacy when publishing sensitive data in maps, but are not well adopted among researchers and may be difficult to execute for some GIS users. We developed a client‐side web application called MaskMy.XYZ that makes geographic masking easy to perform. It executes donut geomasking, a well‐known geographic mask, on thousands of points in seconds, and visualizes the original and masked point patterns in an integrated web map for visual comparison. MaskMy.XYZ also features metrics for both privacy protection and information loss, and allows users to rapidly and iteratively adjust masking parameters based on these metrics. The user interface was designed to prioritize usability, and clear documentation has been included to educate users about geographic masks, which is otherwise only found in niche literatures. By developing this application, we hope that geographic masks will be more widely adopted such that privacy is better protected in research.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".