<i>k</i> -jump: A strategy to design publicly-known algorithms for privacy preserving micro-data disclosure
Bibliographic record
Abstract
Abstract Data owners are expected to disclose micro-data for research, analysis, and various other purposes. In disclosing micro-data with sensitive attributes, the goal is usually two fold. First, the data utility of disclosed data should be maximized for analysis purposes. Second, the private information contained in such data must be to an acceptable level. Typically, a disclosure algorithm evaluates potential generalization functions in a predetermined order, and then discloses the first generalization that satisfies the desired privacy property. Recent studies show that adversarial inferences using knowledge about such disclosure algorithms can usually render the algorithm unsafe. In this paper, we show that an existing unsafe algorithm can be transformed into a large family of safe algorithms, namely, k-jump algorithms. We then prove that the data utility of different k-jump algorithms is generally incomparable. The comparison of data utility is independent of utility measures and syntactic privacy models. Finally, we analyze the computational complexity of k-jump algorithms, and confirm the necessity of safe algorithms even when a secret choice is made among algorithms.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".