Statistical Disclosure Control for Microdata Using the R-Package sdcMicro
Statistical Disclosure Control for Microdata Using the R-Package sdcMicro
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使用 R 包 sdcMicro 对微数据进行统计披露控制
DOI：
10.18637/jss.v067.i04
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
M. Templ
中科院分区：
文献类型：
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作者：
M. Templ
过去几年来，对载有关于个人或企业的合理信息的调查、人口普查或登记数据的需求大幅增加。然而，在向公众或研究人员提供数据之前，必须尊重任何可能包含有关个别单位的合理信息的数据集的保密性。通过对数据应用统计披露控制（SDC）方法来降低数据的披露风险，可以实现机密性。R包sdcMicro作为SDC方法的易于处理的、面向对象的S4类实现，用于评估和匿名化机密微观数据集。它包括所有流行的披露风险和扰动方法。该软件包在每个匿名化步骤后自动重新计算频率计数、个人和全球风险度量、信息丢失和数据实用程序统计数据。所有方法都在计算成本方面进行了高度优化，以便能够处理大型数据集。从业人员也可以轻松使用汇总匿名化过程的报告工具。我们描述的包，并证明其功能与一个复杂的家庭调查测试数据集，已分发的国际家庭调查网。
The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.