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Robust Statistics Meets SDC:New Disclosure Risk Measures for Continuous Microdata Masking

机译：强大的统计数据满足SDC：连续微数据屏蔽的新披露风险衡量

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摘要

The aim of this study is to evaluate the risk of re-identification related to distance-based disclosure risk measures for numerical variables. First, we overview different - already proposed - disclosure risk measures. Unfortunately, all these measures do not account for outliers. We assume that outliers must be protected more than observations near the center of the data cloud. Therefore, we propose a weighting scheme for each observation based on the concept of robust Mahalanobis distances. We also consider the peculiarities of different protection methods and adapt our measures to be able to give realistic measures for each method. In order to test our proposed distance based disclosure risk measures we run a simulation study with different amounts of data contamination. The results of the simulation study shows the usefulness of the proposed measures and gives deeper insights into how the risk of quantitative data can be measured successfully. All the methods proposed and all the protection methods plus measures used in this paper are implemented in R-package sdcMicro which is freely available on the comprehensive R archive network.
机译：这项研究的目的是评估与基于距离的数字变量披露风险度量相关的重新识别风险。首先，我们概述了不同的（已经提出的）披露风险衡量方法。不幸的是，所有这些措施都不能解决异常值。我们认为离群值必须比数据云中心附近的观测值得到更多的保护。因此，我们基于鲁棒的马哈拉诺比斯距离的概念为每个观测结果提出一个加权方案。我们还考虑了不同保护方法的特殊性，并调整了我们的措施，以便能够针对每种方法提供切合实际的措施。为了测试我们建议的基于距离的公开风险度量，我们对不同数量的数据污染进行了模拟研究。仿真研究的结果表明了所提出措施的实用性，并对如何成功地测量定量数据的风险提供了更深刻的见解。本文使用的所有建议方法以及所有保护方法以及措施均在R-package sdcMicro中实现，该软件包可在全面的R归档网络上免费获得。

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