Efficient and model‐agnostic parameter estimation under privacy‐preserving post‐randomization data
Bibliographic record
Abstract
Abstract Balancing data privacy with public access is critical for sensitive datasets. However, even after de‐identification, the data are still vulnerable to, for example, inference attacks (by matching some keywords with external datasets). Statistical disclosure control (SDC) methods offer additional protection, and the post‐randomization method (PRAM) adds noise to data to achieve this goal. However, PRAM‐perturbed data pose challenges for analysis, as directly using the perturbed data leads to biased parameter estimates. This article addresses parameter estimation when data are perturbed using PRAM for privacy. While existing methods suffer from limitations like being parameter‐specific, model‐dependent and lacking optimality guarantees, our proposed method overcomes these limitations. Our approach applies to general parameters defined through estimating equations and makes no assumptions about the underlying data model. Furthermore, we prove that the proposed estimator achieves the semiparametric efficiency bound, making it asymptotically optimal in terms of estimation efficiency.
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".