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Synthetic data is a promising approach to privacy protection in manycontexts. A Bayesian synthesis model, also known as a synthesizer, simulatessynthetic values of sensitive variables from their posterior predictivedistributions. The resulting synthetic data can then be released in place ofthe confidential data. An important evaluation prior to synthetic data releaseis its level of privacy protection, which is often in the form of disclosurerisks evaluation. Attribute disclosure, referring to an intruder correctlyinferring the confidential values of synthetic records, is one type ofdisclosure that is challenging to be computationally evaluated. In this paper,we review and discuss in detail some Bayesian estimation approaches toattribute disclosure risks evaluation, with examples of commonly-used Bayesiansynthesizers. We create the $\texttt{AttributeRiskCalculation}$ R package tofacilitate its implementation, and demonstrate its functionality with examplesof evaluating attribute disclosure risks in synthetic samples of the ConsumerExpenditure Surveys.

Bayesian Estimation of Attribute Disclosure Risks in Synthetic Data with the $\texttt{AttributeRiskCalculation}$ R Package