Privacy of Confidential Numerical Data

Abstract: In this paper, we have proposed a Dilation Based Transformation (DBT) algorithm for securing numerical attributes before they are shared for joint analysis. If we talk about the application of DBT, we have to consider horizontally partitioned data. So if this proposed work gets implemented we are able to preserve the privacy of confidential numerical data. If we are looking data for easily accessible locally by using distribution. We can use it for business growth. So for calculating meaningful, useful, previously unknown data from large databases, we used data mining technique and that for with preserving privacy use for shared data. So clustering on partitioned data and that for with preserving privacy of confidential data has been a vast area of research.

Keywords: DBT, Clustering, Classification, Data mining, Data Matrix.