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Now a dayâ€™s Data publishing process is the compulsory for visualizing the data sets to other parties. But in publishing process there is thread for data set owners by disclosing the sensitive information. For avoiding those problems we using anonymization techniques for secure data publishing. Most Anonymization techniques such as Generalization and Bucketization are using now. But these anonymization techniques have some limitation. Generalization for k-anonymity losses considerable amount of information for high- dimensional data and Bucketization does not prevent membership disclosure, because bucketization publishes the QI values in their original forms, It requires a clear separation between QIâ€™s and SAâ€™s, but in many data sets, it is unclear which attributes are QIâ€™s and which are SAâ€™s and it breaks the attributes correlations between the QIâ€™s and the SAâ€™s. This paper introduces new anonymization technique slicing to overcome all the drawbacks of bucketization and generalization
Keywords: - Sensitive information, High dimensional data, data anonymization, data publishing, data security, generalization and bucketization.
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