A Novel Method for Privacy Preserving Micro data Publishing using Slicing
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 4
Abstract
Data anonymization techniques for privacy-preserving data publishing have received a lot of attention in recent years. Microdata or detailed data contains information about a person, a household or an organization. Most popular anonymization techniques are: Generalization and Bucketization. Generalization transforms the Quasi-Identifiers (QI) in each bucket into “less specific but semantically consistent” values so that tuples in the same bucket cannot be distinguished by their QI- values. In bucketization, one separates the Sensitive Attributes (SAs) from the QIs by randomly permuting the SA values in each bucket. The process of Generalization loses considerable amount of information, especially for highdimensional data. Where as Bucketization does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes. To improve the current state of the art,in this paper, we propose a novel data anonymization technique called slicing. Slicing method preserves utility because it groups highly correlated attributes together, and preserves the correlations between such attributes. Slicing protects privacy because it breaks the associations between the uncorrelated attributes, which are infrequent and thus identifying
Authors and Affiliations
Shaik Khasimbee
Keywords
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