Maintaining Analytic Utility while Protecting Confidentiality of Survey and Nonsurvey Data
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
Consider a complete rectangular database at the micro (or unit) level from a survey (sample or census) or nonsurvey (administrative source) in which potential identifying variables (IVs) are suitably categorized (so that the analytic utility is essentially maintained) for reducing the pretreatment disclosure risk to the extent possible. The pretreatment risk is due to the presence of unique records (with respect to IVs) or nonuniques (i.e., more than one record having a common IV profile) with similar values of at least one sensitive variable (SV). This setup covers macro (or aggregate) level data including tabular data because a common mean value (of 1 in the case of count data) to all units in the aggregation or cell can be assigned. Our goal is to create a public use file with simultaneous control of disclosure risk and information loss after disclosure treatment by perturbation (i.e., substitution of IVs and not SVs) and suppression (i.e., subsampling-out of records). In this paper, an alternative framework of measuring information loss and disclosure risk under a nonsynthetic approach as proposed by Singh (2002, 2006) is considered which, in contrast to the commonly used deterministic treatment, is based on a stochastic selection of records for disclosure treatment in the sense that all records are subject to treatment (with possibly different probabilities), but only a small proportion of them are actually treated. We also propose an extension of the above alternative framework of Singh with the goal of generalizing risk measures to allow partial risk scores for unique and nonunique records. Survey sampling techniques of sample allocation are used to assign substitution and subsampling rates to risk strata defined by unique and nonunique records such that bias due to substitution and variance due to subsampling for main study variables (functions of SVs and IVs) are minimized. This is followed by calibration to controls based on original estimates of main study variables so that these estimates are preserved, and bias and variance for other study variables may also be reduced. The above alternative framework leads to the method of disclosure treatment known as MASSC (signifying micro-agglomeration, substitution, subsampling, and calibration) and to an enhanced method (denoted GenMASSC) which uses generalized risk measures. The GenMASSC method is illustrated through a simple example followed by a discussion of relative merits and demerits of nonsynthetic and synthetic methods of disclosure treatment.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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