DATA SLICING TECHNIQUE TO PRIVACY PRESERVING AND DATA PUBLISHING

Abstract

Many techniques have been designed for privacy pres erving and micro data publishing, such as generaliz ation and bucketization. Several works showed that generalization loses some amount of information especially for high dimensio nal data. So it’s not efficient for high dimensional data. In case of Bucketization , it does not prevents membership disclosure and al so does not applicable for data that do not have a clear separation between Quasi-i dentifying attributes and sensitive attributes. In this paper, we presenting an innovative technique called data slicing which part itions the data. An efficient algorithm is develope d for computing sliced data that obeys l-diversity requirement. we also show how dat a slicing is better than generalization and bucketi zation. Data slicing preserves better utility than generalization and also does no t requires clear separation between Quasi-identifyi ng and sensitive attributes. Data slicing is also used to prevent attribute disclosur e and develop an efficient algorithm for computing the sliced data that obeys ldiversity requirement. Experimental results confirm that data slicing preserves data utility than gene ralization and more effective than bucketization involving sensitive attributes. Exper imental results demonstrate the effectiveness of th is method.