Please use this identifier to cite or link to this item:
Title: A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory
Authors: Bhat, T.P.
Karthik, C.
Chandrasekaran, K.
Issue Date: 2015
Citation: Procedia Computer Science, 2015, Vol.54, , pp.422-430
Abstract: Traditional approaches to data mining may perform well on extraction of information necessary to build a classification rule useful for further categorisation in supervised classification learning problems. However most of the approaches require fail to hide the identity of the subject to whom the data pertains to, and this can cause a big privacy breach. This document addresses this issue by the use of a graph theoretical approach based on k-partitioning of graphs, which paves way to creation of a complex decision tree classifier, organised in a prioritised hierarchy. Experimental results and analytical treatment to justify the correctness of the approach are also included. � 2015 The Authors.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.