Multidimensional Suppression for K-Anonymity in Public Dataset Using See5
B. Sathiya · International Journal of Data Mining Techniques and Applications · 2014
Data mining is a well known technique for many applications. Privacy is very important while handling public datasets. Generalization and suppression are the two methods that are widely used for annonymizing datasets. In generalization the values are replaced with less specific but semantically consistent values and in suppression technique the value is never released. So generalization is applied for many areas since suppression may reduce the accuracy of results if not properly used. Though generalization requires manually generated domain hierarchy taxonomy for every quasi identifier in the dataset on which K-Anonymity is performed. . K-Anonymity is one of the best methods for deidentification of large public datasets.