Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm

  • Shabnum RehmanEmail author
  • Anil Sharma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)


Data mining is a process of extracting knowledge from the large databases. This has made data mining a significant and functional emerging trend. Association rule is one of the most used data mining techniques that discover hidden correlations from huge data sets. There are several mining algorithms for association rules Apriori is one of the most popular algorithm used for extracting frequent item sets from databases and getting the association rule for knowledge discovery. The time required for generating frequent item sets plays an important role. Based on this algorithm we are performing comparison of sanitized data and existing data based on number of iterations and the execution time. The experimental results shows that the number of iteration is reduced in sanitized data than that of existing data also the time is reduced in sanitized data. The association rule generation leads to ensure privacy of the dataset by creating items so, in this way privacy of association rules along with data quality is well maintained.


Data mining Data privacy Association rule mining Apriori algorithm 


  1. 1.
    Tekieh, M.H., Raaheni, B.: Importance of data mining in healthcare: a survey. In: International Conference on Advances in Social Networks Analysis and Mining IEEE (2015)Google Scholar
  2. 2.
    Himel, D., Tanmoy, S., Madhusudan, B., Mohammad, E.A.: An approach to protect the privacy of cloud data from data mining based attacks, pp. 1106–1115. IEEE (2013). doi: 10.1109/SC.Companion.2012.133
  3. 3.
    Dileep, K.S., Vishnu, S.: Data security and privacy in data mining: research issues & preparation. Int. J. Comput. Trends Technol. 4(2), 194–200 (2013)Google Scholar
  4. 4.
    Padhy, N., Mishra, P., Panigrahi, R.: The survey of data mining applications and future scope. Int. J. Comput. Sci. Eng. Inf. Technol. 2(2), 43–58 (2012). doi: 10.5121/ijcseit.2012.2303 Google Scholar
  5. 5.
    Domadiya, N.H., Rao, U.P.: Hiding sensitive association rules to maintain privacy and data quality in database. In: 3rd IEEE International Advance Computing Conference (2013)Google Scholar
  6. 6.
    Agarwal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: International Conference on Management of Data – SIGMOD, pp. 207–216 (1993)Google Scholar
  7. 7.
    Modi, C.N., Rao, U.D., Patel, D.R.: Maintaining privacy and data quality in privacy preserving association rule mining. In: 2nd International Conference on Computing Communication and Networking Technologies IEEE (2010)Google Scholar
  8. 8.
    Zheng, J., Yan, L.: Research on the improvement of Apriori algorithm and its application in intrusion detection system. IEEE, pp. 105–108 (2015)Google Scholar
  9. 9.
    Wu, S., Wang, H.: Research on privacy preserving algorithm of association rule mining in centralized database. In: International Symposium on Information Processing IEEE, pp. 131–134 (2008). doi: 10.1109/ISIP.2008.11
  10. 10.
    Zhang, K., Liu, J., Chai, Y., Zhou, J., Li, Y.: A method to optimize Apriori algorithm for frequent items mining. In: 7th International Symposium on Computational Intelligence and Design IEEE, pp. 71–75 (2014). doi: 10.1109/ISCID.2014.233
  11. 11.
    Ingle, G., Suryavanshi, N.Y.: Association rule mining using improved Apriori algorithm. Int. J. Comput. Appl. 112(4), 37–42 (2015)Google Scholar
  12. 12.
    Yabing, J.: Research of an improved Apriori algorithm in data mining association rules. Int. J. Comput. Commun. Eng. 2(1), 25–27 (2013)CrossRefGoogle Scholar
  13. 13.
    Narmadha, S., Vijayarani, S.: Protecting sensitive association rules in privacy preserving data mining using genetic algorithms. Int. J. Comput. Appl. 33(7), 37–43 (2011)Google Scholar
  14. 14.
    Maolegi, M.A., Arkok, B.: An improved Apriori algorithm for association rules. Int. J. Nat. Lang. Comput. 3(1), 21–29 (2014). doi: 10.5121/ijnlc.2014.3103.21 CrossRefGoogle Scholar
  15. 15.
    Aniket, Y.J., Virendra, R.D., Sagar, S.B., Hardik, P.K.: Privacy preserving association rule mining in retail industries. Int. J. Adv. Res. Comput. Commun. Eng. 4(3) (2015). doi: 10.17148/IJARCCE.2015.4320
  16. 16.
    Afzali, G.A., Shahriar, M.: Privacy preserving big data mining: association rule hiding. J. Inf. Syst. Telecommun. 4(2), 70–77 (2016)Google Scholar
  17. 17.
    Shaofei, W., Hui, W.: Research on the privacy preserving algorithm of association rule mining in centralized database. In: International Symposium on Information Processing IEEE, pp. 131–134 (2008). doi: 10.1109/ISIP.2008.11
  18. 18.
    Linchun, L., Rongxing, L., Kim, K.R.C., Anwitaman, D., Jun, S.: Privacy preserving outsourced association rule mining on vertically partitioned databases. IEEE (2016). doi: 10.1109/TIFS.2016.2561241
  19. 19.
    Pravin, R.P., Jagade, S.M.: Privacy preserving by hiding association rule mining from database. IOSR J. Comput. Eng. 16(5), 25–31 (2014)Google Scholar
  20. 20.
    Adrian, C., Szilvia, L., Andres, L.: Efficient Apriori based algorithm for privacy preserving frequent itemset mining. In: 5th International Conference on Cognitive Info Communications IEEE, pp. 431–435 (2014)Google Scholar
  21. 21.
    Chih, C.W., Shan, T.C., Hung, C.L.: A novel algorithm for completely hiding sensitive association rules. In: 8th International Conference on Intelligent Systems Design & Applications IEEE, pp. 202–208 (2008). doi: 10.1109/ISDA.2008.180
  22. 22.
    Stanly, R.M.O., Osmar, R.Z.: Protecting Sensitive Knowledge by Data SanitizationGoogle Scholar
  23. 23.
    Janakiramaiah, B., RamaMohan, R.A., Kalyani, G.: An approach for privacy preserving in association rule mining using data restriction. Int. J. Eng. Sci. Invention 2(1), 27–34 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Lovely Professional UniversityPhagwaraIndia

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