An efficient masking technique for sensitive data protection

Primary goal of data mining is determining new and precious facts and knowledge from the large databases. Privacy preserving data mining is one of the research areas in the field of data mining which deals with the side effects of the data mining techniques. The results of data mining techniques may produce complete or accurate data; it produces privacy issues regardless of their intended use. Most of the circumstances, the knowledge extracted from the data mining algorithm is highly confidential and it should be modified before giving it to the public. For restricting and protecting the confidential knowledge, first we have to modify the sensitive data items in a data set. Several masking techniques are used for protecting sensitive data items. In this research work, we have analyzed the performance of two perturbative masking techniques namely data transformation technique and bit transformation technique which are used for protecting sensitive numeric data in the micro data table. The experimental result shows that the data transformation technique has produced better results than bit transformation approach.