Correlation hiding by independence masking
Bibliographic record
Abstract
Extracting useful correlation from a dataset has been extensively studied. In this paper, we deal with the opposite, namely, a problem we call correlation hiding (CH), which is fundamental in numerous applications that need to disseminate data containing sensitive information. In this problem, we are given a relational table T whose attributes can be classified into three disjoint sets A, B, and C. The objective is to distort some values in T so that A becomes independent from B, and yet, their correlation with C is preserved as much as possible. CH is different from all the problems studied previously in the area of data privacy, in that CH demands complete elimination of the correlation between two sets of attributes, whereas the previous research focuses on partial elimination up to a certain level. A new operator called independence masking is proposed to solve the CH problem. Implementations of the operator with good worst case guarantees are described in the full version of this short note.
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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 | 0.008 |
| 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 | 0.019 | 0.042 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".