Maintaining Analytic Utility while Protecting Confidentiality of Survey and Nonsurvey Data
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle