An Efficient Method for Protecting High Utility Itemsets in Utility Mining

Privacy preserving data mining (PPDM) has become a popular research direction in data mining. Privacy preserving data mining is an approach to develop algorithms by which we can modify the utility values of original data using some techniques in order to protect sensitive information from unauthorized user. Protecting data against illegal access becomes a serious issue when this data is required to be shared onto the network due to some reasons. To hide the sensitive information, many approaches have been proposed. In this study, we are proposing an efficient method, for protecting high utility itemsets using distortion technique where the values for high utility items are altered to achieve the privacy. Algorithm is designed in such a way so as to handle privacy without disclosure of sensitive information. The algorithm can completely hide any given utility items by scanning data iteratively. The results when compared with existing one show significant reduction in execution time.