Martin Michael K, Helm Julie, Patyk Kelly A
Livestock Poultry Health Division, Clemson University, Columbia, SC 29224, USA.
Livestock Poultry Health Division, Clemson University, Columbia, SC 29224, USA.
Prev Vet Med. 2015 Jun 15;120(2):131-140. doi: 10.1016/j.prevetmed.2015.04.010. Epub 2015 Apr 24.
We describe a method for de-identifying point location data used for disease spread modeling to allow data custodians to share data with modeling experts without disclosing individual farm identities. The approach is implemented in an open-source software program that is described and evaluated here. The program allows a data custodian to select a level of de-identification based on the K-anonymity statistic. The program converts a file of true farm locations and attributes into a file appropriate for use in disease spread modeling with the locations randomly modified to prevent re-identification based on location. Important epidemiological relationships such as clustering are preserved to as much as possible to allow modeling similar to those using true identifiable data. The software implementation was verified by visual inspection and basic descriptive spatial analysis of the output. Performance is sufficient to allow de-identification of even large data sets on desktop computers available to any data custodian.
我们描述了一种对用于疾病传播建模的点位置数据进行去识别处理的方法，以使数据保管人能够与建模专家共享数据，同时不泄露各个农场的身份。该方法在一个开源软件程序中实现，本文将对此软件程序进行描述和评估。该程序允许数据保管人根据K-匿名统计量选择去识别级别。该程序将真实农场位置和属性的文件转换为适合用于疾病传播建模的文件，其中位置会被随机修改，以防止基于位置进行重新识别。诸如聚类等重要的流行病学关系会尽可能地保留，以便进行类似于使用真实可识别数据的建模。通过对输出结果进行目视检查和基本的描述性空间分析，验证了软件的实现。其性能足以让任何数据保管人在台式计算机上对甚至是大型数据集进行去识别处理。
