A NEW ENHANCED SLICING METHOD FOR BOTH NUMERICAL AND CATEGORICAL SENSITIVE ATTRIBUTE VIA ADVANCED CLUSTERING ALGORITHM

Dharavathu. Radha

Abstract


Nowadays Privacy Preserving in Data Publishing (PPDP) has rapidly growth into the research contents in data protection field and also it has rapidly grown in a huge amount of private health data is to be held in present-days. Recent years in data publishing is exceptionally analytical, because, how to economically preserve numerical and categorical sensitive attribute?. However, different tender have been designed for privacy preserving to protect numerical sensitive attribute in data publishing, like k-anonymity, l-diversity, t-closeness, (epsilon, m)-anonymity another methods are implemented for protecting the privacy of data provider. In this paper, we propose that suggested modern technique for “A New Enhanced Slicing Method for both Numerical and Categorical Sensitive Attribute via Advanced Clustering Algorithm”. This technique suitable for both categorical and numerical sensitive attributes. The method vertically and horizontally partitions the multiple numerical and categorical sensitive attributes into several tables, here the tuple partitioning in horizontally and attribute partitioning in vertically. First, generalizes the quasi-identifier (QID) to implement k-anonymity, at the same time, new enhanced slicing technique vertically partitions the multiple sensitive attributes into different tables using the advanced clustering algorithm (ADA). Sequentially to get optimum dimension of buckets and alignment of the co-related sensitive Attribute (SA) in sensitive table together and thereby minimizes the dimensionality. Those improving the privacy with less information loss, membership, attributes, identity disclosure, and also compared less distortion for QID attribute with SLAMSA and SLOMS.


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