Privacy Preserving Hybrid Data Transformation Based On Svd

Abstract: Though data mining has emerged as a significant technology, individual privacy concerns has been growing with the use of this technology. This problem is addressed with a new branch of data mining, known as privacy preserving data mining, which incorporates the mechanisms for protecting sensitive information. In this paper a hybrid data transformation method is proposed for privacy preserving clustering in centralized database environment. The proposed hybrid method takes the advantage of two existing techniques such as Singular Value Decomposition (SVD) and shearing based data perturbation. Experimental results demonstrate that the proposed method efficiently protects the private data of individuals and retains the important information for clustering analysis.

Keywords: Singular value decomposition, Shearing data perturbation, Privacy preserving clustering