Estimating Identification Disclosure Risk Using Mixed Membership Models

Statistical agencies and other organizations that disseminate data are obligated to protect data subjects' confidentiality. For example, ill-intentioned individuals might link data subjects to records in other databases by matching on common characteristics (keys). Successful links are particul...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 107; H. 500; S. 1385 - 1394
Hauptverfasser:	Manrique-Vallier, Daniel, Reiter, Jerome P
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis Group 01.12.2012 Taylor & Francis Ltd
Schlagworte:	Algorithms Applications and Case Studies Bayesian analysis Bias Censuses Confidentiality Contingency table Contingency tables data collection Data sampling Datasets Disclosure Estimate reliability Estimates Estimation methods False positive errors Grade of membership Latent class Linear analysis Linear models Markov analysis Markov chain Membership Membership groups Modeling Monte Carlo simulation Population Population estimates prediction Probability Public use Random sampling risk Risk assessment Statistics U.S.A Uniqueness United States United States Contingency table Grade of membership Latent class Disclosure Confidentiality
ISSN:	1537-274X, 0162-1459, 1537-274X
Online-Zugang:	Volltext
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