This paper proposes and compares measures of identity and attribute disclosure risk for synthetic data. Data custodians can use the methods proposed here to inform the decision as to whether to release synthetic versions of confidential data. Different measures are evaluated on two data sets. Insight into the measures is obtained by examining the details of the records identified as posing a disclosure risk. This leads to methods to identify, and possibly exclude, apparently risky records where the identification or attribution would be expected by someone with background knowledge of the data. The methods described are available as part of the \textbf{synthpop} package for \textbf{R}.

翻译：本文提出并比较了合成数据中身份与属性泄露风险的度量方法。数据管理者可采用本文提出的方法，为是否发布机密数据的合成版本提供决策依据。我们在两个数据集上评估了不同度量指标的有效性。通过深入分析被识别为存在泄露风险的记录细节，我们获得了对各项度量指标的深刻理解。基于此，我们提出了相应方法，用于识别（并可能排除）那些具有数据背景知识的个体可能预期会识别或推断出的明显高风险记录。所述方法已在\textbf{R}语言的\textbf{synthpop}软件包中实现。