EFFICIENT APPROACH FOR PRIVACY PRESERVING MICRODATA PUBLISHING USING SLICING
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 4
Abstract
Many agencies and organizations are willing to release the data they collected to other parties for research and the formulation of public policies. Data often contains personally identifiable information and therefore releasing such data may result in privacy breaches. Several anonymization techniques, like generalization and Bucketization are designed for privacy preserving microdata publishing. Generalization loses considerable amount of information especially for high dimensional data. 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. In this paper we show how slicing can be used for attribute disclosure protection. Slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. It also demonstrates that how overlapping slicing is used to prevent membership disclosure.
Authors and Affiliations
Vani B, D. Jayanthi
Keywords
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