Bayesian Estimation of Disclosure Risks for Multiply Imputed, Synthetic Data

The Journal of Privacy and Confidentiality

卷:6

Biyuan Zhang¹  Quanli Wang²  Jerome P. Reiter² 

[1] Department of Economics, Duke University, Durham, NC;

[2] Department of Statistical Science, Box 90251, Duke University, Durham, NC;

关键词: Bayesian;    confidentiality;    imputation;    risk;    synthetic;   

DOI  :  10.29012/jpc.v6i1.635

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown