Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining

  • Piotr AndruszkiewiczEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9121)


This paper deals with discovering frequent sets for quantitative association rules mining with preserved privacy. It focuses on privacy preserving on an individual level, when true individual values, e.g., values of attributes describing customers, are not revealed. Only distorted values and parameters of the distortion procedure are public. However, a miner can discover hidden knowledge, e.g., association rules, from the distorted data. In order to find frequent sets for quantitative association rules mining with preserved privacy, not only does a miner need to discretise continuous attributes, transform them into binary attributes, but also, after both discretisation and binarisation, the calculation of the distortion parameters for new attributes is necessary. Then a miner can apply either MASK (Mining Associations with Secrecy Konstraints) or MMASK (Modified MASK) to find candidates for frequent sets and estimate their supports. In this paper the methodology for calculating distortion parameters of newly created attributes after both discretisation and binarisation of attributes for quantitative association rules mining has been proposed. The new application of MMASK for finding frequent sets in discovering quantitative association rules with preserved privacy has been also presented. The application of MMASK scheme for frequent sets mining in quantitative association rules discovery on real data sets has been experimentally verified. The results of the experiments show that both MASK and MMASK can be applied in frequent sets mining for quantitative association rules with preserved privacy, however, MMASK gives better results in this task.


Privacy preserving data mining Quantitative association rules Frequent sets Discretisation MMASK 


  1. 1.
    Clifton, C., Marks, D.: Security and privacy implications of data mining. In: ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada, University of British Columbia Department of Computer Science, pp. 15–19 (1996)Google Scholar
  2. 2.
    Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. SIGMOD Rec. 33(1), 50–57 (2004)CrossRefGoogle Scholar
  3. 3.
    Rizvi, S.J., Haritsa, J.R.: Maintaining data privacy in association rule mining. In: VLDB 2002: Proceedings of the 28th International Conference on Very Large Data Bases, VLDB Endowment, pp. 682–693 (2002)Google Scholar
  4. 4.
    Andruszkiewicz, P.: Optimization for MASK scheme in privacy preserving data mining for association rules. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 465–474. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  5. 5.
    Xia, Y., Yang, Y., Chi, Y.: Mining association rules with non-uniform privacy concerns. In: Das, G., Liu, B., Yu, P.S. (eds.) DMKD, pp. 27–34. ACM (2004)Google Scholar
  6. 6.
    Agrawal, S., Krishnan, V., Haritsa, J.R.: On addressing efficiency concerns in privacy preserving data mining. CoRR cs.DB/0310038 (2003)Google Scholar
  7. 7.
    Andruszkiewicz, P.: Hierarchical combining of classifiers in privacy preserving data mining. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 573–584. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  8. 8.
    Andruszkiewicz, P.: Reduction relaxation in privacy preserving association rules mining. In: Morzy, T., Härder, T., Wrembel, R. (eds.) ADBIS 2012. AISC, vol. 186, pp. 1–8. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Chen, Z.Y., hua Liu, G.: Quantitative association rules mining methods with privacy-preserving. In: PDCAT, pp. 910–912. IEEE Computer Society (2005)Google Scholar
  10. 10.
    SathiyaPriya, K., Sadasivam, G.S., Celin, N.: A new method for preserving privacy in quantitative association rules using dsr approach with automated generation of membership function. In: 2011 World Congress on Information and Communication Technologies (WICT), pp. 148–153 (2011)Google Scholar
  11. 11.
    SathiyaPriya, K., Sadasivam, G.S., Aarthi, V.C., Divya, K., Suganya, C.J.P.: Privacy preserving quantitative association rule mining. In: Trends in Innovative Computing 2012 - Intelligent Systems Design, pp. 155–160 (2012)Google Scholar
  12. 12.
    Andruszkiewicz, P.: Probability distribution reconstruction for nominal attributes in privacy preserving classification. In: ICHIT 2008: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology, pp. 494–500. IEEE Computer Society, Washington, DC (2008)Google Scholar
  13. 13.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) SIGMOD Conference, pp. 439–450. ACM (2000)Google Scholar
  14. 14.
    Agrawal, R., Srikant, R., Thomas, D.: Privacy preserving olap. In: SIGMOD 2005: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 251–262. ACM, New York (2005)Google Scholar
  15. 15.
    Andruszkiewicz, P.: Privacy preserving classification with emerging patterns. In Saygin, Y., Yu, J.X., Kargupta, H., Wang, W., Ranka, S., Yu, P.S., Wu, X., (eds.) ICDM Workshops, pp. 100–105. IEEE Computer Society (2009)Google Scholar
  16. 16.
    Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)Google Scholar
  17. 17.
    Srikant, R., Agrawal, R.: Mining generalized association rules. In: Dayal, U., Gray, P.M.D., Nishio, S. (eds.) VLDB, pp. 407–419. Morgan Kaufmann (1995)Google Scholar
  18. 18.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

Personalised recommendations