Privacy and Classification Of Analyzed Data Using EMD
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
References
[1] Tiancheng Li, Ninghui Li, Senior Member, IEEE, Jia Zhang, Member, IEEE, and Ian Molloy
“Slicing: A New Approach for Privacy Preserving Data Publishing†Proc. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 3 MARCH 2012.
J.-W. Byun, Y. Sohn, E. Bertino, and N. Li, “Secure Anonymization for Incremental Datasets,†Proc. VLDB Workshop Secure Data Management (SDM), pp. 48-63, 2006.
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
Refbacks
- There are currently no refbacks.
Copyright © 2013, All rights reserved.| ijseat.com
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.
Â