The individual data may be altered, for a variety of purposes. To overcome these concerns, a number of techniques have recently been proposed. Preserving utility of data and actual data from generalization and bucketization in workload involving the sensitive attributes the new technique introduced 'Slicing'. Slicing can handle high dimensional data by partitioning the data sets horizontally and vertically. In slicing data can be organized arbitrarily, checking privacy threats is a concern. Due to the large size of the data sources having several hundred millions to several billions records, and continuously growing, efficient techniques and algorithms are needed. Slicing preserves better data utility than generalization and also prevents membership disclosure. One approach to speed up the processing is to use a process, where potential candidate records are grouped together one and each group is further processed and analyzed on overlapping attributes. The record grouping problem is a formal formulation is to be done in step one. The significance of using slicing is that it can handle high dimension data. Slicing technique used random rows and columns which not give better accuracy hence the new technique of grouping, which improve the working efficiency and accuracy. This paper focus on effective method that can be used for providing better data utility . It can handle high-dimensional data for better security.

Slicing Partitioning Privacy Preservation Grouping algorithm transitive closure