Slicing: Privacy Preserving Data Publishing Technique
||International Journal of Computer & Organization Trends and Technology (IJCOT)||
|© 2014 by IJCOT Journal|
|Volume - 4 Issue - 1
|Year of Publication : 2014|
|Authors : Ashwini Andhalkar , Pradnya Ingawale|
|DOI : 10.14445/22492593/IJCOT-V5P318|
Ashwini Andhalkar , Pradnya Ingawale. "Slicing: Privacy Preserving Data Publishing Technique", International Journal of Computer & organization Trends (IJCOT), V4(1):59-62 Jan - Feb 2014, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Today, most enterprises are actively collecting and storing data in large databases. Many of them have recognized the potential value of these data as an information source for making business decisions. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. In this paper, a brief yet systematic review of several Anonymization techniques such as generalization and Bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. On the other hand, Bucketization does not prevent membership disclosure. Whereas slicing preserves better data utility than generalization and also prevents membership disclosure. This paper focuses on effective method that can be used for providing better data utility and can handle high dimensional data..
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Data Anonymization, Privacy Preservation, Data publishing, Data Security, PPDP.