Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods

Data objects which do not comply with the general behavior or model of the data are called Outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. However, the use of Outlier Detection for various purposes has raised concerns about the violation of individual privacy. Therefore, Privacy Preserving Outlier Detection must ensure that privacy concerns are addressed and balanced, so that the data analyst can get the benefits of outlier detection without being thwarted by legal counter-measures by privacy advocates. In this paper, we propose a technique for detecting outliers while preserving privacy, using hierarchical clustering methods. We analyze our technique to quantify the privacy preserved by this method and also prove that reverse engineering the perturbed data is extremely difficult.

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