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Association Rule Hiding Using Firefly Optimization Algorithm

  • S. SharmilaEmail author
  • S. Vijayarani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Privacy preserving data mining is an important research area which protects the private information and reduces the information loss during the data mining process. There are many data mining techniques whereas Association rule mining is one of the data mining technique which finds existing correlations between data items. Privacy Preserving Association Rule Mining is one of the techniques in this field, which aims to hide sensitive association rules. Many different algorithms with particular approaches have been developed to protect the private information. In this paper, a new approach has been introduced using firefly optimization algorithm for hiding the sensitive association rules. To hide the sensitive rules distortion technique was used. Further in this work fitness function was defined to achieve the optimal solution with fewest side effects. The efficiency of proposed algorithm was evaluated with different databases. The results of the execution of the proposed algorithm and existing algorithm tabu search on different databases indicates that firefly algorithm has better performance compared to other algorithm.

Keywords

Data mining Privacy preserving data mining Association rule hiding Firefly optimization algorithm 

References

  1. 1.
    Agarwal, C.C., Yu, P.S. (eds.): Privacy-Preserving Data Mining: Modeland Algorithms (2008). ISBN 0-387-70991-8Google Scholar
  2. 2.
    Jain, Y.K.: An efficient association rule hiding algorithm for privacy-preserving data mining. Int. J. Comput. Sci. Eng. 3(7), 2792–2798 (2011)Google Scholar
  3. 3.
    Nayak, G., Devi, S.: A survey on privacy preserving data mining: approaches and techniques. Int. J. Eng. Sci. Technol. 3(3), 2127–2133 (2011)Google Scholar
  4. 4.
    Patel Tushar, S., Mayur, P., Dhara, L., Jahnvi, K., Piyusha, D., Ashish, P., Reecha, P.: Association an analytical study of various frequent itemset mining algorithms. Res. J. Comput. Inf. Technol. Sci. 1(1), 6–9 (2013). February Res. J. Computer & IT Sci. International Science CongressGoogle Scholar
  5. 5.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Mateo (2006)zbMATHGoogle Scholar
  6. 6.
    Saygin, Y., Verykios, V.S., Elmagarmid, A.K.: Privacy preserving association rule mining. In: Proceedings of the 2002 International (2002)Google Scholar
  7. 7.
    Schuster, A., Wolff, R., Gilburd, B.: Privacy preserving data mining on data grids in the presence of malicious participants. In: IEEE International Symposium on High Performance Distributed Computing - HPDC (2004)Google Scholar
  8. 8.
    Zhang, N., Wang, S., Zhao, W.: A new scheme on privacy preserving association rule mining. In: Principles of Data Mining and Knowledge Discovery – PKDD, vol. 3202, pp. 484–495 (2004)Google Scholar
  9. 9.
    Otey, M.E., Wang, C., Parthasarathy, S., Veloso, A., Meria, W.: Mining frequent itemsets in distributed and dynamic databases. In: IEEE International Conference on Data Mining (2003)Google Scholar
  10. 10.
    Afshari, M.H., Dehkordi, M.N., Akbari, M.: Association rule hiding using cuckoo optimization algorithm. Expert Syst. Appl. 64, 340–351 (2016)CrossRefGoogle Scholar
  11. 11.
    Dehkordi, M.N., Badie, K., Zadeh, A.K.: A novel method for privacy preserving in association rule mining based on genetic algorithms. J. Softw. 4(6), 555–562 (2009)Google Scholar
  12. 12.
    Yang, X.S.: A discrete firefly algorithm for the multi-objective hybrid flow shop scheduling problems. IEEE Trans. Evol. Comput. 18(2), 301–305 (2014)Google Scholar
  13. 13.
    Jia, D., Duan, X., Khan, M.K.: Binary artificial bee colony optimization using bitwise operation. Comput. Ind. Eng. 76, 360–365 (2014)CrossRefGoogle Scholar
  14. 14.
    Khan, A., Qureshi, M.S., Hussain, A.: Improved genetic algorithm approach for sensitive association rules hiding. World Appl. Sci. J. 31(12), 2087–2092 (2014)Google Scholar
  15. 15.
    Le, H.Q., Arch-Int, S., Nguyen, H.X., Arch-Int, N.: Association rule hiding in risk management for retail supply chain collaboration. Comput. Ind. 64(7), 776–784 (2013)CrossRefGoogle Scholar
  16. 16.
    Oliveira, S.R.M., Zaiane, O.R.: Privacy preserving frequent itemset mining. In: Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, vol. 14, pp. 43–54 (2002)Google Scholar
  17. 17.
    Vijayarani, S., Tamilarasi, A., SeethaLakshmi, R.: Tabu search based association rule hiding. Int. J. Comput. Appl. 19(1), 0975–8887 (2011)Google Scholar
  18. 18.
    Yuan, F., Chen, S., Liu, H.: Association rules mining on heart failure differential treatment based on the improved firefly algorithm. J. Comput. 9(4), 822–830 (2014)Google Scholar
  19. 19.
    Neelima, S., Sathyanarayan, N., Murthy, P.K.: A novel multi-objective firefly algorithm for optimization of association rule mining (2017)Google Scholar
  20. 20.
    Telikani, A., Shahbahrami, A.: Data sanitization in association rule mining: an analytical review. Expert Syst. 96, 406–426 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceBharathiar UniversityCoimbatoreIndia

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