In the field of statistical disclosure control, the tradeoff between data confidentiality and data utility is measured by comparing disclosure risk and information loss metrics. Distance based metrics such as the mean absolute error (MAE), mean squared error (MSE), mean variation (IL1), and its scaled alternative (IL1s) are popular information loss measures for numerical microdata. However, the fact that these measures are unbounded makes it is difficult to compare them against disclosure risk measures which are usually bounded between 0 and 1. In this note, we propose rank-based versions of the MAE and MSE metrics that are bounded in the same range as the disclosure risk metrics. We empirically compare the proposed bounded metrics against the distance-based metrics in a series of experiments where the metrics are evaluated over multiple masked datasets, generated by the application of increasing amounts of perturbation (e.g., by adding increasing amounts of noise). Our results show that the proposed bounded metrics produce similar rankings as the traditional ones (as measured by Spearman correlation), suggesting that they are a viable additions to the toolbox of distance-based information loss metrics currently in use in the SDC literature.

翻译：在统计披露控制领域，数据保密性与数据效用之间的权衡通过比较披露风险与信息损失度量来实现。距离基度量如平均绝对误差（MAE）、均方误差（MSE）、平均变异（IL1）及其缩放版本（IL1s）是数值型微观数据常用的信息损失度量。然而，这些度量无界性的特点使得它们难以与通常界于0和1之间的披露风险度量进行比较。本文提出MAE和MSE的秩次版本，使其与披露风险度量具有相同的有界范围。我们通过一系列实验对新提出的有界度量与距离基度量进行经验比较，这些实验基于多个通过施加递增扰动（如加入递增噪声）生成的掩蔽数据集评估度量性能。结果表明，新提出的有界度量与传统度量产生相似的秩次排序（以Spearman相关性衡量），表明它们可作为SDC文献中当前使用的距离基信息损失度量工具箱的有效补充。