Limiting sensitive values in an anonymized table while reducing information loss via <i>p</i>‐proportion
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
Abstract The ‐proportion model bounds the proportion of sensitive values of a sensitive attribute in each equivalence class of an anonymized database table in order to limit the ability of a user to link an individual or entity to a sensitive value in that table. Nonsensitive values are not subject to any such constraints, which reduces the amount of anonymization needed to meet the requirements of this model. This leads to less information loss in an anonymized table. Anonymization is performed using an extension of the Mondrian algorithm that incorporates categorical attributes. Known as the adapted Mondrian algorithm, it generalizes a value of a categorical attribute to a set. Existing algorithms, by comparison, replace one value of a predefined hierarchy by another. The ‐proportion model is compared against the ( )‐anonymity model using both the progressive local recoding and (adapted) Mondrian algorithms. Experiments demonstrate the advantage of ‐proportion and Mondrian over ( )‐anonymity and progressive local recoding in terms of reduced information loss, measured using the normalized certainty penalty, discernibility metric, and classification metric.
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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 | 0.006 |
| 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 | 0.005 |
| Open science | 0.003 | 0.017 |
| 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