CLUST-SVD: Privacy preserving clustering in singular value decomposition

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may encounter when releasing data to outside parties. It brings out 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 Clustering Singular Value Decomposition (CLUST-SVD) distorts only confidential numerical attributes to meet privacy requirements, while preserving general features for k-means clustering analysis.