Geographic Partitioning Techniques for the Anonymization of Health Care Data
Bibliographic record
Abstract
As the demand for the availability of detailed health care data sets continues to increase, organizations are faced with the conflicting interests of releasing this important information while protecting the confidentiality of the individuals to whom the data pertains.A major concern when releasing health care data is the geographic information which has a large influence on the re-identifiability of the data and yet is essential for many research applications.In this work, a novel system for health care data anonymization is presented.At the core of the system is the aggregation of an initial regionalization guided by the use of a Voronoi diagram.The process is broken up into major components for which different approaches are presented and tested.Testing is conducted via an implementation designed to run and analyze the results of the various combinations of approaches.In addition, a comparative test is conducted with another application, GeoLeader, which uses an alternative process for anonymization through geographic aggregation.It is shown that the Voronoi system is capable of producing comparable results with a much faster running time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".