Estimating Identification Disclosure Risk Using Mixed Membership Models

DOI10.1080/01621459.2012.710508zbMath1258.62029OpenAlexW2002352118WikidataQ28655921 ScholiaQ28655921MaRDI QIDQ4904716

Daniel Manrique-Vallier, Jerome P. Reiter

Publication date: 31 January 2013

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4159106

zbMATH Keywords

confidentialitycontingency tableslatent classgrade of membership

Mathematics Subject Classification ID

Bayesian inference (62F15) Sampling theory, sample surveys (62D05) Numerical analysis or methods applied to Markov chains (65C40) Contingency tables (62H17)

Related Items (6)

Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation ⋮ An information theoretic approach to post randomization methods under differential privacy ⋮ Comparative study of differentially private data synthesis methods ⋮ Optimal disclosure risk assessment ⋮ Bayesian nonparametric disclosure risk assessment ⋮ Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models

Cites Work

• Unnamed Item
• Assessing Identification Risk in Survey Microdata Using Log-Linear Models
• The Multiple Adaptations of Multiple Imputation
• Conditional association and unidimensionality in monotone latent variable models
• Statistical confidentiality. Principles and practice.
• Assessing the protection provided by misclassification-based disclosure limitation methods for survey microdata
• A correlated topic model of science
• A mixture of experts model for rank data with applications in election studies
• Alternative statistical models and representations for large sparse multi-dimensional contingency tables
• Using the conditional grade-of-membership model to assess judgment accuracy
• Comparing latent structures of the grade of membership, Rasch, and latent class models
• Polyhedral conditions for the nonexistence of the MLE for hierarchical log-linear models
• Describing disability through individual-level mixture models for multivariate binary data
• Statistical methods for some simple disclosure limitation rules
• 10.1162/jmlr.2003.3.4-5.993
• Exploratory latent structure analysis using both identifiable and unidentifiable models
• Proposals for 2001 Samples of Anonymized Records: An Assessment of Disclosure Risk
• Item Response Theory: Past Performance, Present Developments, and Future Expectations
• Parameterization and Bayesian Modeling
• Estimating Risks of Identification Disclosure in Microdata

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