Disclosure Risk of Geography Attributes: The Role of Spatial Scale, Identified Geography, and Measurement Detail in Public-Use Files
Bibliographic record
Abstract
Spatial information is essential for modern forms of analysis; and as a result, researchers have increasingly called for geographically-specific microdata. Contextual data is one way to safely release this information without identifying the location of survey respondents. Analyzing an array of geography attributes, I conduct reidentification experiments for 14,796 simulated datasets to measure the likelihood of pinpointing geographic locations under alternative database designs, relating to: (1) the spatial scale of standard geographies, as determined by the areal size of these administrative units; (2) the scope of study, as determined by the identification of division, state, and MSA-status; (3) the number of geography attributes provided in a dataset; and (4) and coarseness of these contextual measures, as determined by global recoding schema. Using the “data file” as my unit of analysis, the number of geographic units resembling a study location as the outcome of interest, and associated experimental traits, I detail the complexity of reidentification patterns that emerge when constructing public-use files that provide contextual data where two distinct scenarios of intruder search behavior are assumed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".