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A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining
September 2006 (vol. 18 no. 9)
pp. 1278-1283
Sumit Sarkar, IEEE Computer Society
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.

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Index Terms:
Privacy, data mining, data perturbation, microaggregation, kd-trees.
Xiao-Bai Li, Sumit Sarkar, "A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 9, pp. 1278-1283, Sept. 2006, doi:10.1109/TKDE.2006.136
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