Абстрактный

Privacy Preserving Clustering Based on Discrete Cosine Transformation

M. Naga lakshmi, K Sandhya Rani

The information related to an individual or an organization could be compromised when the patterns extracted from large databases through data mining technology. Privacy preserving data mining which is a new research area has been evolved in order to find the right balance between maximizing analysis results and minimizing the disclosure of private information. In this paper, a Discrete Cosine Transformation (DCT) based data distortion method is proposed for privacy preserving clustering in centralized database environment. The experimental results proved that the proposed method efficiently protects the private data of individuals and retains the important information for clustering analysis.