Data Publishing Thorough Micao Grouping Tranformation with Privacy Utility Preservation

Abstract

Now days data exchanging between two parties need secrete communication, there is a need to handle the risk of unintended information disclosure. Encrypting the data while sending is not easy job always, without revealing sensitive information about them is an important problem. K-anonymization is the most valuable method among other data protection techniques. The limitations of K- anonymity were surmount by methods like L-diversity, T-closeness, (alpha, K) anonymity; but all of these methods focus on universal approach that exerts the same amount of privacy preservation for all persons against linking attack, which result in high loss of information. Privacy was also not guaranteed 100% because of proximity and divergence attack. In this paper Our approach is to design micro data sanitization technique to preserve privacy against proximity and divergence attack and also to preserve the utility of the data for any type of mining task. The proposed approach, apply a graded grouping transformation on numerical sensitive attribute and a mapping table based transformation on categorical sensitive attribute. We conduct experiments on adult data set and compare the results of original

 and transformed table to show that the proposed task independent technique preserves privacy, information and utility.