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The synthetic data approach to data confidentiality has been activelyresearched on, and for the past decade or so, a good number of high qualitywork on developing innovative synthesizers, creating appropriate utilitymeasures and risk measures, among others, have been published. Comparing to alarge volume of work on synthesizers development and utility measures creation,measuring risks has overall received less attention. This paper focuses on thedetailed construction of some Bayesian methods proposed for estimatingdisclosure risks in synthetic data. In the processes of presenting attributeand identification disclosure risks evaluation methods, we highlight key steps,emphasize Bayesian thinking, illustrate with real application examples, anddiscuss challenges and future research directions. We hope to give the readersa comprehensive view of the Bayesian estimation procedures, enable syntheticdata researchers and producers to use these procedures to evaluate disclosurerisks, and encourage more researchers to work in this important growing field.

Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data