A tree-based data perturbation approach for privacy-preserving data mining

被引：32

作者：

IEEE Computer Society ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

机构：

[1] College of Management, University of Massachusetts Lowell, Lowell

[2] School of Management, University of Texas at Dallas, Richardson

来源：

IEEE Trans Knowl Data Eng | 2006年 / 9卷 / 1278-1283期

关键词：

Data mining; Data perturbation; Kd-trees; Microaggregation; Privacy;

D O I：

10.1109/TKDE.2006.136

中图分类号：

学科分类号：

摘要：

Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method. © 2006 IEEE.

引用

页码：1278 / 1283

页数：5

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