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Protecting Sensitive Association Rules in Privacy Preserving Data Mining using Genetic Algorithms

International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 7
Year of Publication: 2011

S.Narmadha and S.Vijayarani. Article: Protecting Sensitive Association Rules in Privacy Preserving Data Mining using Genetic Algorithms. International Journal of Computer Applications 33(7):36-43, November 2011. Full text available. BibTeX

	author = {S.Narmadha and S.Vijayarani},
	title = {Article: Protecting Sensitive Association Rules in Privacy Preserving Data Mining using Genetic Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {7},
	pages = {36-43},
	month = {November},
	note = {Full text available}


Privacy and security risks taken place from the application of different data mining techniques to great organizational data storehouses have been exclusively inspected by a new research domain, the so-called privacy preserving data mining. Association rule hiding is one of the privacy preserving techniques which study the problem of hiding sensitive association rules. There are many algorithms and techniques were developed to solve this problem. In this research work, we have used genetic algorithm optimization technique for protecting sensitive association rules.


  • Abedelaziz Mohaisen and Dowon Hong. 2008. Privacy Preserving Association Rule Mining Revisited. Journal of the Computing Research Repository.
  • Atallah, M., Elmagarmid, A., Ibrahim, M., Bertino, E., Verykios. V. 1999. Disclosure limitation of sensitive rules. Workshop on Knowledge and Data Engineering Exchange.
  • Bikramjit Saikia, Debkumar Bhowmik. Study of Association Rule Mining And different hiding Techniques. Department of computer Science Engineering, National Institute of Technology,Rourkela.
  • Charu C. Aggarwal and Philip S. Privacy-preserving data mining: Models and Algorithms. ISBN: 0-387-70991-8.
  • Colin R.Reeves, Jonathan E.Rowe. 2002. Genetic algorithms principles and perspectives.
  • Darrell Whitley. 1994. A genetic algorithm tutorial. Colorado State University.
  • Juggapong Natwichai, Xingzhi Sun, and Xue . 2008. A Heuristic Data Reduction Approach for Associative Classification Rule Hiding. Pacific rim international conference on artificial inteligence-PRICAI.
  • Mohammad Naderi Dehkordi. 2009. A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms. Journal of software-JSW, volume 4,no 6.
  • Nan Zhang, Shengquan Wang, and Wei Zhao. 2004. A New Scheme on Privacy Preserving Association Rule Mining. Principles of Data Mining and Knowledge Discovery – PKDD, Volume 3202, Pg: 484-495,
  • Rakesh Agrawal,Tomasz lmielinski,Arun Swami. Mining Association Rules between sets of items in Large Databases.IBM Almaden Research Center,San Jose,CA 95120.
  • R.R.Rajalaxmi. A Novel Sanitization Approach for Privacy Preserving Utility Itemset Mining”, Computer Science and Engineering Kongu Engineering College Erode, TamilNadu, India.
  • Shyue-Liang Wang Yu-Huei Lee Billis, S. Jafari. 2006. A. Hiding Sensitive items in Privacy Preserving Association rule Mining.
  • Vaidya, j.Clifton, .W; Zhu, Y.M 2006, X, 121 p, 20 illus, Hardcover..Privacy Preserving Data Mining. ISBN: 979-0-387-25886-7.
  • Vassilios S. Verykios • Emmanuel D. Pontikakis • Yannis Theodoridis • Liwu Chang. 2007. Efficient algorithms for distortion and blocking techniques in association rule hiding, springer.
  • Yucel Saygin, Vassilios S.Verkios, Ahmed K. Elmagarmid,. 2002.Privacy Preserving Association Rule Mining. Conference of Research Issues in Data Engineering – RIDE.