This paper introduces two methods of creating differentially private (DP) synthetic data that are now incorporated into the \textit{synthpop} package for \textbf{R}. Both are suitable for synthesising categorical data, or numeric data grouped into categories. Ten data sets with varying characteristics were used to evaluate the methods. Measures of disclosiveness and of utility were defined and calculated The first method is to add DP noise to a cross tabulation of all the variables and create synthetic data by a multinomial sample from the resulting probabilities. While this method certainly reduced disclosure risk, it did not provide synthetic data of adequate quality for any of the data sets. The other method is to create a set of noisy marginal distributions that are made to agree with each other with an iterative proportional fitting algorithm and then to use the fitted probabilities as above. This proved to provide useable synthetic data for most of these data sets at values of the differentially privacy parameter $\epsilon$ as low as 0.5. The relationship between the disclosure risk and $\epsilon$ is illustrated for each of the data sets. Results show how the trade-off between disclosiveness and data utility depend on the characteristics of the data sets.
翻译:本文介绍了两种方法,即为\ textit{synthpop}软件包中输入了用于\ textbf{R} 的不同私人合成数据(DP) 。这两种方法都适合于合成绝对数据,或将数字数据分组为类别。使用10个具有不同特点的数据集来评估方法。界定和计算了分散性和实用性衡量标准。第一个方法是将DP噪音添加到所有变量的交叉列表中,并用由此产生的概率的多数值样本生成合成数据。虽然这种方法肯定降低了披露风险,但它没有为任何数据集提供质量适当的合成数据。另一个方法是创建一套噪音边缘分布图,以相互一致,同时采用迭代比例相配算算法,然后使用上述配配配的概率。这证明能为大多数这些数据集提供可使用的合成数据,以差分隐私参数值($\epsilon)值的0.5美元为低值。每个数据集的披露风险和$\epslon美元之间的关系是说明的。每个数据集的通用性特征都说明。结果显示贸易数据集的可靠性。