The basic idea of slicing is to break the association cross columns, but to preserve the association within each column. This reduces the dimensionality of the data and preserves better utility than generalization and bucketization. Slicing preserves utility because it groups highly correlated attributes together, and preserves the correlations between such attributes. Proposed system efficient slicing algorithm to achieve -diverse slicing. Given a microdata table T and two parameters c and , the algorithm computes the sliced table that consists of c columns and satisfies the privacy requirement of -diversity. For measuring the correlation coefficient using pearson and chi squared correlation coefficient in attribute partitioning step for -diversity slicing. Slicing protects privacy because it breaks the associations between uncorrelated attributes, which are infrequent and thus identifying. Proposed system work in the following manner: attribute partitioning, attribute clustering, tuple partitioning and Analyzing the slicing using Noise enabled slicing. In first step for performing the attribute partitioning ,First compute the correlations between pairs of attributes and sensitive attributes on their correlations using the Chi squared and Pearson based correlation coefficient and then cluster attributes based on their correlations using the Chi squared and Pearson based correlation coefficient .It improves the accuracy of the system for partitioning the result, After these steps finished we perform ,By evaluation of the result by adding the noise data to sensitive attributes for both Chi squared and Pearson based L-diversity slicing. Experimental results shows that the proposed system improves the data utility and privacythentheexistingslicingmethods