A. Kannagi, M. Muthuraja, Pavai College of Technology, Namakkal, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.05.09
International Journal of Advanced Information and Communication Technology
Received On : December 10, 2016
Revised On : January 23, 2016
Accepted On : February 15, 2016
Published On : March 05, 2016
Volume 03, Issue 03
Pages : 461-465
Abstract
In a data distribution scenario the sensitive data given to agents can be leaked in some cases and can be found in unauthorized places. Our aim is to detect when the distributor’s sensitive data have been leaked by agents and to identify the agent who leaked the data. We consider the addition of fake objects to the distributed set which do not correspond to real entities but appear realistic to the agents. The distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been independently gathered by other means. We also present data allocation strategies and algorithms for distributing objects to agents, in a way that improves our chances of identifying a leaker. Our main idea is to prevent the agents from comparing their data with one another to identify fake objects. A Symmetric Inference Model (SIM) is used here to find out the probability of identifying dependency among the data distributed to various agents. Using this technique a symmetric inference graph (SIG) is drawn denoting the links among data sets.
Keywords
Symmetric Inference Model, Symmetric Inference Graph, Sensitive Data.
Cite this article
A. Kannagi, M. Muthuraja, “Data Security Description of Enhanced Data Mining Analysis using Symmetric Inference Model” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.461-465, March 05, 2016.
Copyright
© 2016 A. Kannagi, M. Muthuraja. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.