Privacy and Classification Of Analyzed Data Using EMD

T Sravya, G Appa Rao

Abstract


In recent years many researchers issued on data publishing with recommended settings .But privacy is a key issue here. Existing techniques such as K-anonymity and L-diversity should not provide effective and sufficient results for privacy preserving in data publishing. So in this paper we propose tree base algorithm for providing security, In this technique we arrange the data in tree based format for closeness of a data publishing and for retrieving data in sequential order. Our techniques also improved more security to micro data publishing and retrieving relevant information from micro data using attribute disclosure.


Keywords


Privacy, data publishing, K-anonymity, L-diversity

References


[1] Tiancheng Li, Ninghui Li, Senior Member, IEEE, Jia Zhang, Member, IEEE, and Ian Molloy

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B.-C. Chen, K. LeFevre, and R. Ramakrishnan, “Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge,” Proc. Int’l Conf. Very Large Data Bases (VLDB), pp. 770- 781, 2007.

G. T. Duncan, S. E. Fienberg, R. Krishnan, R. Padman, and S.

G. T. Duncan and D. Lambert. Disclosure limited data dissemination. J. Am. Stat. Assoc., pages 10–28, 1986


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