On optimal anonymization for l<sup>+</sup>-diversity
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
Publishing person specific data while protecting privacy is an important problem. Existing algorithms that enforce the privacy principle called l-diversity are heuristic based due to the NP-hardness. Several questions remain open: can we get a significant gain in the data utility from an optimal solution compared to heuristic ones; can we improve the utility by setting a distinct privacy threshold per sensitive value; is it practical to find an optimal solution efficiently for real world datasets. This paper addresses these questions. Specifically, we present a pruning based algorithm for finding an optimal solution to an extended form of the l-diversity problem. The novelty lies in several strong techniques: a novel structure for enumerating all solutions, methods for estimating cost lower bounds, strategies for dynamically arranging the enumeration order and updating lower bounds. This approach can be instantiated with any reasonable cost metric. Experiments on real world datasets show that our algorithm is efficient and improves the data utility.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.023 | 0.070 |
| 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