SVDC: Preserving Privacy in Clustering using Singular Value Decomposition

Abstract

AbstractProtecting privacy from unauthorized access is one of the primary concerns in data use, from national security to business transactions. It creates a new branch of data mining known as Privacy Preserving Data Mining (PPDM). 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; we propose a Singular Value Decomposition (SVD) method for data distortion. We focus primarily on privacy preserving data clustering. Our proposed method, Singular Value Decomposition Clustering (SVDC) distorts only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis.