Privacy Preservation of Infrequent Itemsets Mining Using GA Approach

  • Sunidhi ShrivastavaEmail author
  • Punit Kumar Johari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 555)


Privacy preservation of information is an important approach to data mining. Infrequent or rare itemset mining is a new technique in this field which is very useful for gaining profit from the business point of view. Rare thing can make more profit. Misuse of these techniques can lead to revelation of confidential information. In this paper, we addressed this problem of privacy preservation of data mining by using sanitization of database or in the other word hiding high utility rare itemsets. We have identified high utility rare patterns and introduce an approach for dynamic addition of transactions. The central goal of the proposed algorithm is to optimize high utility rare items for providing privacy.


Privacy preservation Data mining Rare itemset mining Utility mining Genetic algorithm 


  1. 1.
    Jain, Nikita, and Vishal Srivastava. “Data Mining techniques: A survey paper.” IJRET: International Journal of Research in Engineering and Technology 2.11 (2013): 2319–1163.Google Scholar
  2. 2.
    Janakiramaiah, B., A. Rama Mohan Reddy, and G. Kalyani. “Privacy Preserving Frequent Itemset Mining by Reducing Sensitive Items Frequency using GA”.Google Scholar
  3. 3.
    Joshi, Maya, and Mansi Patel. “A Survey on High Utility Itemset Mining Using Transaction Databases.” Vol. 5 (6), 2014, 7407–7410.Google Scholar
  4. 4.
    Tseng, Vincent S., et al. “Efficient algorithms for mining high utility itemsets from transactional databases.” Knowledge and Data Engineering, IEEE Transactions on 25.8 (2013): 1772–1786.Google Scholar
  5. 5.
    Lin, Jerry Chun-Wei, et al. “Efficient algorithms for mining high-utility itemsets in uncertain databases.” Knowledge-Based Systems (2016).Google Scholar
  6. 6.
    Sudip Bhattacharya, Deepty Dubey, “High Utility Itemset Mining”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 8, August 2012.Google Scholar
  7. 7.
    H. Yao and H. J. Hamilton, “Mining itemset utilities from transactio databases,” Data and Knowledge Engineering, vol. 59, pp. 603–626 2006.Google Scholar
  8. 8.
    Endu Duneja and A.K. Sachan, “A Survey on Frequent Itemset Mining with Association Rules”, International Journal of Computer Applications (0975 – 8887) Volume 46– No. 23, May 2012.Google Scholar
  9. 9.
    Sethi, Nidhi, and Pradeep Sharma. “Efficient Algorithms for Mining Rare Itemset over Time Variant Transactional Database.” International Journal Of Computer Science and Information Technologies 5 (2014).Google Scholar
  10. 10.
    Srinivas, Mandavilli, and Lalit M. Patnaik. “Genetic algorithms: A survey.” Computer 27.6 (1994): 17–26.Google Scholar
  11. 11.
    R.K. Battacharya, Introduction to Genetic Algorithm, Indian Institute of Technology, Guwahati, 2012.Google Scholar
  12. 12.
    Hong Yao, Howard J. Hamilton, Liqiang Geng, “A Unified Framework for Utility Based Measures for Mining itemsets”, In Proc. of the ACM Intel. Conf. on Utility-Based Data Mining Workshop (UBDM), pp. 28–37, 2006.Google Scholar
  13. 13.
    Jianying Hu, Aleksandra Mojsilovic, “High-utility pattern mining: A method for discovery of high-utility item sets”, Pattern Recognition 40 (2007) 3317–3324.Google Scholar
  14. 14.
    Jyothi Pillai, O.P. Vyas, “High Utility Rare Itemset Mining (HURI): An Approach for Extracting High-Utility Rare Item Sets.” Journal on Future Engineering and Technology 7, no. 1 (2011).Google Scholar
  15. 15.
    Vincent S. Tseng, Chun-Jung Chu, Tyne Liang, “Efficient Mining of Temporal High Utility Itemsets from Data streams”, Proceedings of Second International Workshop on Utility-Based Data Mining, August 20, 2006.Google Scholar
  16. 16.
    S.A.R. Niha, Dr Uma N Dulhare, “Extraction of High Utility Rare Itemsets from Transactional Databases.” Computer and Communications Technologies (ICCCT), 2014 International Conference on. IEEE, 2014.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Madhav Institute of Technology and ScienceGwaliorIndia

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