Privacy Preserving Clustering on Distorted data

In designing various security and privacy related data mining applications, privacy preserving has become a major concern. Protecting sensitive or confidential information in data mining is an important long term goal. An increased data disclosure risks may encounter when it is released. Various data distortion techniques are widely used to protect sensitive data; these approaches protect data by adding noise or by different matrix decomposition methods. In this paper we primarily focus, data distortion methods such as singular value decomposition (SVD) and sparsified singular value decomposition (SSVD). Various privacy metrics have been proved to measure the difference between original dataset and distorted dataset and degree of privacy protection. The data mining utility k-means clustering is used on these distorted datasets. Our experimental results use a real world dataset. An efficient solution is achieved using sparsified singular value decomposition and singular value decomposition, meeting privacy requirements. The accuracy while using the distorted data is almost equal to that of the original dataset. KeywordsPrivacy Preserving, Data Distortion, Singular Value Decomposition (SVD), Sparsified Singular Value Decomposition (SSVD), k--means clustering.