Adaptive areal elimination (AAE): A transparent way of disclosing protected spatial datasets
Adaptive areal elimination (AAE): A transparent way of disclosing protected spatial datasets
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DOI：
10.1016/j.compenvurbsys.2016.01.004
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发表时间：
2016-05-01
影响因子：
6.8
通讯作者：
Leitner, Michael
中科院分区：
文献类型：
--
作者：
Kounadi, Ourania;Leitner, Michael
地理掩蔽是保护涉及机密空间点数据集的个人隐私的传统解决方案。掩蔽过程取代了机密位置，以保护个人隐私，同时保持良好的空间分辨率。该过程的自适应形式旨在通过考虑底层人口密度来进一步最小化位移误差。我们描述了一种替代的自适应geomasking方法，称为自适应区域消除（AAE）。AAE创建一个最小的K-匿名的区域，然后原始点在区域内随机扰动或聚集到区域的中值中心。除了掩蔽点之外，K匿名化区域也可以安全地公开，而不会增加重新识别的风险。使用盗窃数据集从维也纳，AAE与现有的自适应地理面具，甜甜圈面具。掩模方法进行评估，以保持预定义的K-匿名性和原始点的空间特征。空间特征的评估与空间误差的四个措施：位移距离，密度表面的相关系数，热点的分歧，和集群的特异性。除位移距离外，AAE点集的掩蔽点在所有测量中具有最大的空间误差。相比之下，圆环掩模中的掩模点位移最小，更好地保持了原始空间聚类，具有最高的聚类特异性和密度曲面的相关系数。然而，当环形掩模是适应实现一个实际的K-匿名，随机扰动的AAE引入空间误差小于环形掩模的空间误差的所有措施。(C)2016作者爱思唯尔有限公司出版
Geographical masking is the conventional solution to protect the privacy of individuals involved in confidential spatial point datasets. The masking process displaces confidential locations to protect individual privacy while maintaining a fine level of spatial resolution. The adaptive form of this process aims to further minimize the displacement error by taking into account the underlying population density. We describe an alternative adaptive geomasking method, referred to as Adaptive Areal Elimination (AAE). AAE creates areas of a minimum K-anonymity and then original points are either randomly perturbed within the areas or aggregated to the median centers of the areas. In addition to the masked points, K-anonymized areas can be safely disclosed as well without increasing the risk of re-identification. Using a burglary dataset from Vienna, AAE is compared with an existing adaptive geographical mask, the donut mask. The masking methods are evaluated for preserving a predefined K-anonymity and the spatial characteristics of the original points. The spatial characteristics are assessed with four measures of spatial error: displaced distance, correlation coefficient of density surfaces, hotspots' divergence, and clusters' specificity. Masked points from point aggregation of AAE have the highest spatial error in all the measures but the displaced distance. In contrast, masked points from the donut mask are displaced the least, preserve the original spatial clusters better, have the highest clusters' specificity and correlation coefficient of density surfaces. However, when the donut mask is adapted to achieve an actual K-anonymity, the random perturbation of AAE introduces less spatial error than the donut mask for all the measures of spatial error. (C) 2016 The Authors. Published by Elsevier Ltd.