A Modified KACTUS Algorithm Based Multi-Dimensional Suppression for K-Anonymity

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A.S. Loganayaki
Dr.B.Srinivasan, Mr.P.Narendran

Abstract

Data mining is the process of extracting hidden information from database. The current trend in business collaboration shares the data and mined results to gain mutual benefit. The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms to leverage this information. Two common manipulation techniques used to achieve k-anonymity of a dataset are generalization and suppression. K-Anonymity of Classification Trees Using Suppression (kACTUS) is observed to provide good results in achieving k-anonymity. In KACTUS efficient multidimensional suppression is performed, that is values are suppressed only on certain records depending on other attribute values, without the need for manually-produced domain hierarchy trees. The k-anonymity models is extended by providing new definitions and use several anonymization techniques together in order to get better results in terms of accuracy than reported in the literature.


Keywords: KACTUS 2; k-anonymity; privacy preserving; decision tree; computational complexity

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