A Secure Protocol to Maintain Data Privacy in Data Mining

  • Fodé Camara
  • Samba Ndiaye
  • Yahya Slimani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5678)


Recently, privacy issues have becomes important in data mining, especially when data is horizontally or vertically partitioned. For the vertically partitioned case, many data mining problems can be reduced to securely computing the scalar product. Among these problems, we can mention association rule mining over vertically partitioned data. Efficiency of a secure scalar product can be measured by the overhead of communication needed to ensure this security. Several solutions have been proposed for privacy preserving association rule mining in vertically partitioned data. But the main drawback of these solutions is the excessive overhead communication needed for ensuring data privacy. In this paper we propose a new secure scalar product with the aim to reduce the overhead communication.


Association Rule Communication Overhead Secure Protocol Privacy Preserve Homomorphic Encryption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Vaidya, J.S., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23-26, pp. 639–644 (2002)Google Scholar
  2. 2.
    Oliveira, S.R.M., Zaiane, O., Saygin, Y.: Secure Association Rule Sharing. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS, vol. 3056, pp. 74–85. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Saygin, Y., Verykios, V., Clifton, C.: Using Unknowns to prevent discovery of Association Rules. ACM SIGMOD Record 30(4) (2001)Google Scholar
  4. 4.
    Guo, Y.H., Tong, Y.H., Tang, S.W., Yang, D.Q.: Knowledge hiding in database. Journal of Software 18(11), 2782–2799 (2007)CrossRefGoogle Scholar
  5. 5.
    Yao Andrew, C.C.: Protocols for secure computations. In: Proc. of the 23rd Annual IEEE Symposium on Foundations of Computer Science, Chicago, Illinois, November 1982, pp. 160–164 (1982)Google Scholar
  6. 6.
    Yao, A.C.C.: How to generate and exchange secrets. In: Proc. of the 27th Symposium on Foundations of Computer Science (FOCS), Toronto, Canada, October 1986, pp. 162–167 (1986)Google Scholar
  7. 7.
    Goldreich, O.: Secure multi-party computation - working draft (2000),
  8. 8.
    Du, W., Atallah, M.J.: Secure multi-party computation problems and their applications: a review and open problems. In: New Security Paradigms Workshop, Cloudcroft, New Mexico, September 2001, pp. 11–20 (2001)Google Scholar
  9. 9.
    Pinkas, B.: Cryptographic Techniques for Privacy-Preserving Data Mining. ACM SIGKDD Explorations 4(2) (2002)Google Scholar
  10. 10.
    Clifton, C., Kantarcioglu, M.: Tools for privacy preserving distributed data mining. SIGKDD Explorations 4(2), 28–34 (2003)CrossRefGoogle Scholar
  11. 11.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 1993, pp. 207–216 (1993)Google Scholar
  12. 12.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, September 1994, pp. 487–499 (1994)Google Scholar
  13. 13.
    Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On private scalar product computation for privacy-preserving data mining. In: Park, C.-s., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, pp. 104–120. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Amirbekyan, A., Estivill-Castro, V.: A New Efficient Privacy-Preserving Scalar Product Protocol. In: Proc. Sixth Australasian Data Mining Conference (AusDM 2007), Gold Coast, Australia (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fodé Camara
    • 1
  • Samba Ndiaye
    • 1
  • Yahya Slimani
    • 2
  1. 1.Department of Mathematics and Computer ScienceCheikh Anta Diop UniversityDakar
  2. 2.Department of Computer ScienceFaculty of Sciences of TunisTunisTunisia

Personalised recommendations