A STUCTURE TO USE DIFFERENT ANONYMIZATION TECHNIQUES FOR PRIVACY PRESERVING DATA PBLISHING

 this paper we show the framework of Slicing with Tuple grouping algorithm which partitioned the data both horizontally and vertically. It provides better information utility than generalization and Bucketization. Privacy preservation is important for publishing the personal information. Generally personal information records will violate the privacy. So many more techniques have been introduced for privacy preservation. Many anonymization techniques like generalization and bucketization have been designed and developed for privacy preservation. But they have some disadvantages. In this survey paper, we present technique called as slicing, which partitions the data both horizontally and vertically. We experimentally show that how slicing preserves better data information utility than generalization and handle in high dimensional data and protect from membership disclosure