Performance Analysis of Clustering in Privacy Preserving Data Mining

Privacy is becoming an increasingly important issue in many data mining applications. This has triggered the development of many privacy preserving data mining techniques. 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. Existing perturbation methods typically require that the statistical properties of the data can be specified with known distributions. We propose a tree-based perturbation method that can be easily used for perturbing data with knowing the underlying distributions. Our method employs a kd-tree technique to recursively partition a dataset into smaller subsets such that data records within each subset are more homogeneous after each partition. Once the partitioning process is completed, the confidential data in each subset are perturbed using microaggregation. An experimental study shows that our proposed method outperforms additive and multiplicative noise perturbation methods for clustering applications.