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A Rule based Slicing Approach to Achieve Data Publishing and Privacy


C. Saravanabhavan , AMS Engineering College, Namakkal; P. Bhuvaneswari, AMS Engineering College, Namakkal


Privacy preservation, data anonymization, data publishing, data security


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. Bucketization, on the other hand, 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. The existing system proposed slicing concept to overcome the tuple based partition this has been done to overcome the previous generalization and bucketization. In this paper, present a novel technique called rule based slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the l-diversity requirement. The workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. The experiments also demonstrate that slicing can be used to prevent membership disclosure

Other Details

Paper ID: IJSRDV1I4040
Published in: Volume : 1, Issue : 4
Publication Date: 01/07/2013
Page(s): 982-984

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