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A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm

by K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 12
Year of Publication: 2012
Authors: K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan
10.5120/9743-4295

K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan . A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm. International Journal of Computer Applications. 60, 12 ( December 2012), 12-19. DOI=10.5120/9743-4295

@article{ 10.5120/9743-4295,
author = { K. Sathiya Priya, G. Sudha Sadasivam, V. B. Karthikeyan },
title = { A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 12 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number12/9743-4295/ },
doi = { 10.5120/9743-4295 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:21.784806+05:30
%A K. Sathiya Priya
%A G. Sudha Sadasivam
%A V. B. Karthikeyan
%T A New Method for preserving privacy in Quantitative Association Rules using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 12
%P 12-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the process of extracting hidden patterns from data. With the explosion of data, data mining is essential to extract useful information. Association rule mining is a method for finding correlation among large set of data items. A rule is characterized as sensitive if its disclosure risk is above a certain confidence value. Sensitive rules should not be disclosed to the public, as they can be used to infer sensitive data and provide an advantage for the business competitors. Techniques for hiding association rules are almost limited to binary items. But, real world data mostly consists of quantitative values. In this paper, a method to hide fuzzy association rule is proposed, in which, the fuzzified data is mined using modified apriori algorithm in order to extract rules and identify sensitive rules. The sensitive rules are hidden by decreasing the support value of Right Hand Side(RHS) of the rule. Genetic algorithm is used to ensure security of the database and keep the utility and certainty of the mined rules at highest level. Experimental results of the proposed approach demonstrate efficient information hiding with less side effects.

References
  1. Chris Clifton and Murat Kantarcioglu and Jaideep Vaidya, "Defining Privacy for Data Mining," in Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, November 1-3, 2002, Baltimore, MD.
  2. Yucel Saygin, Vassilios Verykios, and Chris Clifton, "Using Unknowns to Prevent Discovery of Association Rules", SIGMOD Record 30 (2001), no. 4, 45–54
  3. Vassilios S. Verykios, A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni, "Association Rule Hiding," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 4, pp. 434-447, 2004.
  4. Chih-Chia Weng, et et. , "A Novel Algorithm for Completely Hiding Sensitive Frequent Itemset" , Dept. of Information Science, Chung Cheng Institute of Technology, National Defense University , 2007
  5. Chih-Chia Weng, Shan-Tai Chen, Hung-Che Lo , "A Novel Algorithm for Completely Hiding Sensitive Association Rules" , Eighth International Conference on Intelligent Systems Design and Applications, 2008
  6. S. L. Wang, and A. Jafari, "Using unknowns for hiding sensitive predictive association rules," In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration (IRI 2005), pp. 223–228, 2005
  7. Yuhong Guo, 2007, "Reconstruction-Based Association Rule Hiding", Proceedings of SIGMOD2007 Ph. D. Workshop on Innovative Database Research 2007(IDAR2007), 51-56
  8. Dr. Duraiswamy. K, Dr. Manjula. D, and Maheswari. N "A New Approach to Sensitive Rule Hiding", ccsenet journal, vol 1, No. 3, August, 107-111
  9. Mohammad Naderi Dehkordi, Kambiz Badie, Ahmad Khadem Zadeh, "A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms", Journal of software, vol. 4, no. 6, August 2009
  10. T. Berberoglu and M. Kaya, "Hiding Fuzzy Association Rules in Quantitative Data",The 3rd InternationalConference on Grid and Pervasive Computing Workshops, May 2008, pp. 387-392.
  11. Manoj Gupta and R. C. Joshi, "Privacy Preserving Fuzzy Association Rules in in Quantitative Data", International Journal of Computer Theory and Engineering, Vol. 1, No. 4, October, 2009, 382-388
  12. T. P. Hong, C. Y. Lee, "Induction of fuzzy rules and membership functions from training examples", Fuzzy Sets and Systems - FSS , vol. 84, no. 1, pp. 33-47, 1996
  13. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim and V. Verykios. "Disclosure limitation of sensitive rules. " Proc. of IEEE Knowledge and Data Engineering Exchange Workshop (KDEX), November 1999.
  14. Cano, J. and P. Nava, "A Fuzzy Method for Automatic Generation Of Membership Function Using Fuzzy Relations from Training Examples", Proceedings of the 21st NAFIPS International Conference, pp. 158-162, June 2002.
  15. Chirag Modi. N, Udai Pratap Rao and Dhiren Patel. R, "An Efficient Solution for Privacy Preserving Association Rule Mining", International Journal of Computer and Network Security, Vol. 2, No. 5, May 2010, 79-85
  16. K. Sathiyapriya, G. Sudhasadasiavm, N. Celin, "A New Method for preserving privacy in Quantitative Association Rules Using DSR Approach With Automated Generation of Membership Function", In the Proceedings of World Congress on Information and Communication Technologies, Mumbai 2011, pp. 148-153. Dec. 2011
  17. D. E. Goldberg, Genetic Algorithms: in Search, Optimization, and Machine Learning. New York :Addison-Wesley Publishing Co. Inc. 1989.
  18. L. A. Zadeh, "Fuzzy Sets", Information and Control, Vol. 8, pp. 338-353, 1965.
  19. http://mlearn. ics. uci. edu/databases/breast-cancer-wisconsin/breast-cancer-wisconsin. data
Index Terms

Computer Science
Information Sciences

Keywords

Association Rules Data Mining Fuzzy Logic Sensitive Rules membership Function

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