Data Distortion for Privacy Protection in a Terrorist Analysis System

Shuting Xu, Jun Zhang, Dianwei Han, and Jie Wang
Laboratory for High Performance Scientific Computing and Computer Simulation
Department of Computer Science
University of Kentucky
Lexington, KY 40506-0046, USA


Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets.

Key words: Privacy protection, counterterrorism singular value decomposition

Mathematics Subject Classification:

Download the compressed postscript file, or the PDF file xu6.pdf.
Technical Report 432-05, Department of Computer Science, University of Kentucky, Lexington, KY, 2005.

The research work was supported in part by the Kentucky New Economy Safety and Security (NESSI) Consortium.