DATA SLICING TECHNIQUE TO PRIVACY PRESERVING AND DATA PUBLISHING

Alphonsa Vedangi, V.Anandam

Abstract: Many techniques have been designed for privacy preserving and micro data publishing, such as generalization and bucketization. Several works showed that generalization loses some amount of information especially for high dimensional data. So it’s not efficient for high dimensional data. In case of Bucketization, it does not prevents membership disclosure and also does not applicable for data that do not have a clear separation between Quasi-identifying attributes and sensitive attributes. In this paper, we presenting an innovative technique called data slicing which partitions the data. An efficient algorithm is developed for computing sliced data that obeys l-diversity requirement. we also show how data slicing is better than generalization and bucketization. Data slicing preserves better utility than generalization and also does not requires clear separation between Quasi-identifying and sensitive attributes. Data slicing is also used to prevent attribute disclosure and develop an efficient algorithm for computing the sliced data that obeys ldiversity requirement. Experimental results confirm that data slicing preserves data utility than generalization and more effective than bucketization involving sensitive attributes. Experimental results demonstrate the effectiveness of this method.

Keywords: Privacy preserving, Data Security, Data Publishing, Microdata.

DOI: https://doi.org/10.15623/ijret.2013.0210016