A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters

  • Vikas G. Ashok
  • Ravi Mukkamala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8566)


Several secure distributed data mining methods have been proposed in the literature that are based on privacy preserving set operation mechanisms. However, they are limited in the scalability of both the size and the number of data owners (sources). Most of these techniques are primarily designed to work with two data owners and extensions to handle multiple owners are either expensive or infeasible. In addition, for large datasets, they incur substantial communication/computation overhead due to the use of cryptographic techniques. In this paper, we propose a scalable privacy-preserving protocol that approximates global itemset support, without employing any cryptographic mechanism. We also present some emperical results to demonstrate the effectiveness of our approach.


Privacy Preserving Set Union Protocol Privacy Preserving Data Mining Secure Multiparty Computation 


  1. 1.
    Liu, K., Kargupta, H., Ryan, J.: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. IEEE Trans. Knowledge and Data Engg. 18(1), 92–106 (2006)CrossRefGoogle Scholar
  2. 2.
    Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Kantarcioglu, M., Nix, R., Vaidya, J.: An efficient approximate protocol for privacy-preserving association rule mining. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 515–524. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), November 19-22. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  5. 5.
    Bloom, B.H.: Space/time Trade-offs in Hash coding with Allowable Errors. Communications of the ACM 13(7), 422–426 (1970)CrossRefzbMATHGoogle Scholar
  6. 6.
    Qiu, L., Li, Y., Wu, X.: Preserving privacy in association rule mining with Bloom filters. Journal of Intelligent Information Systems 29(3), 253–278 (2007)CrossRefGoogle Scholar
  7. 7.
    Vaidya, J., Clifton, C.: Secure set intersection cardinality with application to association rule mining. Journal of Computer Security 13(4), 593–622 (2005)Google Scholar
  8. 8.
    Kissner, L., Song, D.: Privacy-preserving set operations. In: Shoup, V. (ed.) CRYPTO 2005. LNCS, vol. 3621, pp. 241–257. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Andrei, A., Mitzenmacher, M.: Network applications of Bloom filters: A survey. Internet Mathematics 1(4), 485–509 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Goldreich, O.: Foundations of Cryptography. Basic Applications, vol. 2. Cambridge University Press (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Vikas G. Ashok
    • 1
  • Ravi Mukkamala
    • 2
  1. 1.State University of New YorkStony BrookUSA
  2. 2.Old Dominion UniversityNorfolkUSA

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