Upkeeping secrecy in information extraction using ‘k’ division graph based postulates

The prevailing mechanisms for extracting useful information might offer enhanced results in extraction of useful data for creating classification policies. The goal is to administer the disputes prevailing within the categorization for supervised data. Moreover several schemes conceal the individuality of the schemes employed which attempts to conceal the location of information which might become a serious issue during conserving privacy of the data stored. The aim is to address the disputes by making use of a graph and hypothetical based scheme termed as k-segmentation of graphs which delivers the creation of difficult choice based tree classification organized into a priority based hierarchy. The analysis depicts that the designed scheme offers accuracy and effectiveness.

[1]  Raymond Chi-Wing Wong,et al.  (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.

[2]  Chris Clifton,et al.  Privacy-preserving data mining: why, how, and when , 2004, IEEE Security & Privacy Magazine.

[3]  Marisa W. Paryasto,et al.  Big-data security management issues , 2014, 2014 2nd International Conference on Information and Communication Technology (ICoICT).

[4]  Ramakrishnan Srikant,et al.  Privacy-preserving data mining , 2000, SIGMOD '00.

[5]  S. Smys,et al.  Construction of virtual backbone to support mobility in MANET — A less overhead approach , 2009, 2009 International Conference on Application of Information and Communication Technologies.

[6]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[7]  Neal Koblitz,et al.  a course in number theory , 1987 .

[8]  Zhengxin Chen,et al.  Privacy-Preserving Data Mining of Medical Data Using Data Separation-Based Techniques , 2007, Data Sci. J..

[9]  Jung Tae Kim Privacy and Security Issues for Healthcare System with Embedded RFID System on Internet of Things , 2014 .

[10]  Pingshui Wang Survey on Privacy Preserving Data Mining , 2010 .

[11]  Chunxiao Jiang,et al.  Information Security in Big Data: Privacy and Data Mining , 2014, IEEE Access.

[12]  Animesh Tripathy,et al.  A novel framework for preserving privacy of data using correlation analysis , 2012, ICACCI '12.

[13]  Bhavana Abad,et al.  A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity , 2012 .