High-Dimensional Confidential Data Mash up using Service- Oriented Architecture
Pradeep Gurunathan1, N. Ishwarya2, V. Sridevi3, C. Nandhini4, S. Deepalakshmi5
1Pradeep Gurunathan, Professor& Head, Department of Information Technology, A.V.C College of Engineering, Mannamapandal, Mayiladuthurai, Nagapattinam District, Tamilnadu, India.
2Ms.N.Ishwarya, B.Tech. Student, Department of Information Technology, A.V.C College of Engineering, Mannamapandal, Mayiladuthurai, Nagapattinam District, Tamilnadu, India.
3Ms.V.Sridevi, B.Tech. Student, Department of Information Technology, A.V.C College of Engineering, Mannamapandal, Mayiladuthurai, Nagapattinam District, Tamilnadu, India.
4Ms.C.Nandhini, B.Tech. Student, Department of Information Technology, A.V.C College of Engineering, Mannamapandal, Mayiladuthurai, Nagapattinam District, Tamilnadu, India.
5Ms.S.Deepa Lakshmi, B.Tech. Student, Department of Information Technology, A.V.C College of Engineering, Mannamapandal, Mayiladuthurai, Nagapattinam District, Tamilnadu, India.
Manuscript received on March 11, 2013. | Revised Manuscript Received on March 12, 2013. | Manuscript published on March 25, 2013. | PP: 48-51 | Volume-1, Issue-6, April 2013. | Retrieval Number: F0253041613/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mash up is integrating different service providers to expertise and to deliver highly customizable services to their customers. Simply joining multiple private data sets together would reveal the sensitive information to the other data providers. The integrated (mash up) data could potentially sharpen the identification of persons and therefore, expose their personspecific sensitive information that was not available before the mash up. The mash up data from multiple sources often contains many data attributes. When enforcing a established privacy model such as K-anonymity, the high-dimensional data would asssit from the problem known as the curse of high dimensionality, resulting in ineffective data for further data analysis. In this paper, we introduced a new algorithm called Modified privacy preserving high dimensional confidential (MPHDC) mash up algorithm to provide the high dimensional security to the user from the data provider.
Keywords: Confidential mash up, High dimensionality, Mash up service, etc.,