A REVIEW ON ANONYMIZATION TECHNIQUES FOR PRIVACY PRESERVING DATA PUBLISHING

Abstract

With the increased and vast use of online data, privacy in data publishing has now become very important issue. Now days, many organizations are collecting and storing huge volumes of data in large databases. Data publisher collected data from data holders then data publisher released this data to data recipient for research analysis and mining purpose. The released data reveals some information, which is considered to be private and personal. Privacy of information in such scenario becomes subject of research. In recent years, data anonymization techniques become subject of research. In this paper, we provide a review of the statistical Anonymization techniques that can be used for preserving privacy of publish data. Microdata publishing consist of variety of different anonymization methods like Generalization, Bucketization and Suppression. But for high dimensional data generalization loses significant amount of data and affected from curse of dimensionality. Whereas, bucketization fails to preserve membership disclosure and it needs a clear difference between quasi attributes and Sensitive attributes. Suppression reduces quality of data drastically. To deal with these problems, slicing method is introduced. Slicing is new anonymization technique for preserving publish data. In this data is partitioning horizontally as well as vertically. Dimensionality of the data is decreased by slicing. Utility is preserved & correlations between attributes which are highly-correlated together are maintained. Compare to generalization, slicing provides better utility of data. Compare with bucketization, slicing is more effective. High-dimensional data can be handled by slicing. Slicing also Provides attribute disclosure and membership disclosure.