An Approach for Providing Privacy of Microdata Publishing
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 12
Abstract
In recent years, the wide accessibility of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. Privacy preserving for data publishing is achieved through the anonymization techniques such as generalization and bucketization. These previous methods could not give a satisfactory result. For high dimensionality data generalization losses considerable amount of information and bucketization does not prevent the membership disclosure. In this paper, we present a new idea slicing, which protects the data by partitioning the data both horizontally and vertically. We justify that slicing preserves the data integrity, protects from the attribute disclosure and can handle high dimensional data. This can be used in protection of attribute revealing and develop an algorithm to obey the ℓ-diversity requirement. Our experiment shows that slicing gives a better and effective utility than the existing one.
Authors and Affiliations
Illa Manga Devi, BPN Madhu Kumar
Keywords
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