A Classification based Dependent Approach for Suppressing Data

Data mining plays an important role in internet with the computer technology this makes easy to collect the information from the related data sets. The different methods used in this paper are decision tree algorithm, the decision tree algorithm used hears is to classify the data elements by considering a set of constraints, we consider this method to suppress the data by doing so we can secure the data. We extend our work on micro data suppression (1) to prevent not only probabilistic but also decision tree classification based inference, and (2) to handle not only single but also multiple confidential data value suppression to reduce the side-effects. The paper aims to enhance the Data classification and Data Generalization. It shows that how the data is secured using ‗Generalization' and moreover. It provides efficiency in Data Generalization and discusses some of the major challenges for what kind of data to be suppressed. We consider the following privacy problem: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration. Enumerating all candidate generalizations is impractical.