Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data

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Jerome P. Reiter
https://orcid.org/0000-0002-8374-3832
Quanli Wang
Biyuan Zhang

Abstract

Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data. We present a famework for evaluating disclosure risks inherent in releasing multiply-imputed, synthetic data. The basic idea is to mimic an intruder who computes posterior distributions of confidential values given the released synthetic data and prior knowledge. We illustrate the methodology with artificial fully synthetic data and with partial synthesis of the Survey of Youth in Custody.

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How to Cite
Reiter, Jerome, Quanli Wang, and Biyuan Zhang. 2014. “Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data”. Journal of Privacy and Confidentiality 6 (1). https://doi.org/10.29012/jpc.v6i1.635.
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