Title :
Privacy Preserving Outlier Detection Using Hierarchical Clustering Methods
Author :
Challagalla, Ajay ; Dhiraj, S. S Shivaji ; Somayajulu, D.V.L.N. ; Mathew, Toms Shaji ; Tiwari, Saurav ; Sharique Ahmad, Syed
Author_Institution :
Dept. of Comput. Sci. & Eng., Nat. Inst. of Technol., Warangal, India
Abstract :
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.
Keywords :
data mining; data privacy; pattern clustering; hierarchical clustering method; privacy preserving outlier detection; Data Mining; Data Perturbation; Hierarchical Clustering; Outlier Detection; Privacy Preservation;
Conference_Titel :
Computer Software and Applications Conference Workshops (COMPSACW), 2010 IEEE 34th Annual
Conference_Location :
Seoul
Print_ISBN :
978-1-4244-8089-0
Electronic_ISBN :
978-0-7695-4105-1
DOI :
10.1109/COMPSACW.2010.35