A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes
A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes
复制标题
DOI：
10.1186/1476-072x-13-16
复制
发表时间：
2014-05-29
影响因子：
4.9
通讯作者：
El Emam, Khaled
中科院分区：
文献类型：
--
作者：
Jung, Ho-Won;El Emam, Khaled
背景：提出了一种线性规划（LP）模型来创建最大限度地包括空间细节（例如，例如，在一个实施例中，地理编码，如邮政编码、人口普查区和地图上的位置），同时符合HIPAA隐私规则的专家确定方法，即，确保重新识别的风险非常小。LP模型确定从患者的原始位置到新的随机化位置的转移概率。然而，它对人口较少地区的情况有限制（例如，例如，在一个实施例中，中位数10人在一个邮政编码）。方法：我们扩展了以前的LP模型，以适应在某些地方的人口较少的情况下，同时创建去识别的患者空间数据集，以确保重新识别的风险是非常小的。结果：我们的LP模型被应用到一个数据集的11,740邮政编码在城市的渥太华，加拿大。在这个数据集上，我们证明了以前的LP模型的局限性，因为它产生不太可能的结果，并展示了我们的扩展，以处理小面积允许deidentification的整个dataset.Conclusions：在这项研究中描述的LP模型可以用来deidentification的地理空间信息的地区，人口少，失真最小的邮政编码。我们的LP模型可以扩展到包括其他信息，如年龄和性别。
Background: A linear programming (LP) model was proposed to create de-identified data sets that maximally include spatial detail (e. g., geocodes such as ZIP or postal codes, census blocks, and locations on maps) while complying with the HIPAA Privacy Rule's Expert Determination method, i.e., ensuring that the risk of re-identification is very small. The LP model determines the transition probability from an original location of a patient to a new randomized location. However, it has a limitation for the cases of areas with a small population (e. g., median of 10 people in a ZIP code).Methods: We extend the previous LP model to accommodate the cases of a smaller population in some locations, while creating de-identified patient spatial data sets which ensure the risk of re-identification is very small.Results: Our LP model was applied to a data set of 11,740 postal codes in the City of Ottawa, Canada. On this data set we demonstrated the limitations of the previous LP model, in that it produces improbable results, and showed how our extensions to deal with small areas allows the de-identification of the whole data set.Conclusions: The LP model described in this study can be used to de-identify geospatial information for areas with small populations with minimal distortion to postal codes. Our LP model can be extended to include other information, such as age and gender.