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Privacy and Utility in Data Publishing with Full Functional Dependencies

Author(s): P.V.N. Prasoona#1 , M. Vasumathi Devi*2 and K.V. Narasimha Reddy

Journal: International Journal of Engineering Trends and Technology
ISSN 2231-5381

Volume: 4;
Issue: 5;
Start page: 1961;
Date: 2013;
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Keywords: Privacy-preserving | data publishing | functional dependency | utility | data reconstruction.

A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. One of the methods for privacy preserving data mining is that of anonymization, in which a record is released only if it is indistinguishable from k other entities in the data. Data publishing has generated much concern on individual privacy. Recent work has shown that different background knowledge can bring various threats to the privacy of published data. We distinguish the safe FFDs that will not jeopardize privacy from the unsafe ones. We design robust algorithms that can efficiently anonymize the microdata with low information loss when the unsafe FFDs are present. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility

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