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