Advertisement

Large-Scale k-Means Clustering with User-Centric Privacy Preservation

  • Jun Sakuma
  • Shigenobu Kobayashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)

Abstract

A k-means clustering with new privacy-preserving concept, user-centric privacy preservation, is presented. In this framework, users can conduct data mining using their private information with storing them in their local storages. After the computation, they obtain only mining result without disclosing private information to others. The number of parties that join conventional privacy-preserving data mining has been assumed to be two. In our framework, we assume large numbers of parties join the protocol, therefore, not only scalability but also asynchronism and fault-tolerance is important. Considering this, we propose a k-mean algorithm combined with a decentralized cryptographic protocol and a gossip-based protocol. The computational complexity is O( logn) with respect to the number of parties n and experimental results show that our protocol is scalable even with one million parties.

Keywords

privacy data mining clustering k-means peer-to-peer 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 557–570 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 20–24. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Evfimievski, A., Srikant, A., Agrawal, R., Gehrke, J.: Privacy Preserving Mining of Association Rules. In: ACM SIGKDD Int’l conf. on Knowledge discovery in data mining, pp. 217–228 (2002)Google Scholar
  4. 4.
    Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: ACM SIGKDD Int’l conf. on Knowledge discovery in data mining, pp. 206–215 (2003)Google Scholar
  5. 5.
    Jha, S., Kruger, L., McDaniel, P.: Privacy Preserving Clustering. In: European Symposium on Research in Computer Security, pp. 397–417 (2005)Google Scholar
  6. 6.
    Jagannathan, G., Wright, R.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: ACM SIGKDD Int’l conf. on Knowledge discovery in data mining, pp. 593–599 (2005)Google Scholar
  7. 7.
    Kowalczyk, W., Vlassis, N.: Newscast EM. In: NIPS 17, MIT Press, Cambridge (2005)Google Scholar
  8. 8.
    Yao, A.C.-C.: How to Generate and Exchange Secrets. In: IEEE Symposium on FOCS, pp. 162–167 (1986)Google Scholar
  9. 9.
    Kempe, D., Dobra, A., Gehrke, J.: Computing aggregate information using gossip. In: IEEE Symposium on FOCS, pp. 482–491 (2003)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Goldreich, O.: Foundations of Cryptography II: Basic Applications. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jun Sakuma
    • 1
  • Shigenobu Kobayashi
    • 1
  1. 1.Tokyo Institute of TechnologyYokohamaJapan

Personalised recommendations