Fuzzy Based Approach for Privacy Preserving in Data Mining

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-45 Number-1
Year of Publication : 2017
Authors : Shrikant Zade, Dr. Pradeep Chouksey,Dr.R.S.Thakur
  10.14445/22312803/IJCTT-V45P111

MLA

Shrikant Zade, Dr. Pradeep Chouksey,Dr.R.S.Thakur "Fuzzy Based Approach for Privacy Preserving in Data Mining". International Journal of Computer Trends and Technology (IJCTT) V45(1):54-58, March 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Data privacy is the major issue in privacy preserving. It confirms that data of individual publish without disclosing sensitive data of that person. The most popular scheme, is k-anonymity, where data is transformed into equivalence classes, each class having a set of k- records that are indistinguishable from each other. But several authors have pointed out number of issues with k-anonymity and have proposed techniques to counter them or avoid them. The l-diversity and t-closeness are another technique for the same. We studied all possible technique with computational efforts, though they increase privacy, some techniques have too much of information loss, while achieving privacy. In this paper, we propose a novel, holistic approach to achieve maximum privacy with no information loss and minimum overheads. We address the data privacy problem using fuzzy inference system approach by using Gaussian membership transform function, a total paradigm shift and a new perspective of looking at privacy problem in privacy preserving data mining. Our approach is for both numerical and categorical attribute.

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Keywords
Privacy preserving, data privacy, fuzzy inference system, Gaussian membership function