Distributed Data Mining Bibliography Apa

  • 1.

    Abraham, I., Dolev, D., Gonen, R., Halpern, J.: Distributed computing meets game theory: Robust mechanisms for rational secret sharing and multiparty computation. In: PODC 2006, Denver, CO, pp. 53–62 (2006)Google Scholar

  • 2.

    Agrawal, R., Terzi, E.: On honesty in sovereign information sharing. In: Grust, T., Höpfner, H., Illarramendi, A., Jablonski, S., Mesiti, M., Müller, S., Patranjan, P.-L., Sattler, K.-U., Spiliopoulou, M., Wijsen, J. (eds.) EDBT 2006. LNCS, vol. 4254, pp. 240–256. Springer, Heidelberg (2006)CrossRefGoogle Scholar

  • 3.

    Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., Zhu, M.Y.: Tools for privacy preserving distributed data mining. ACM SIGKDD Explorations 4(2), 1–7 (2003)Google Scholar

  • 4.

    Jiang, W., Clifton, C.: Transforming semi-honest protocols to ensure accountability. In: PADM 2006, Hong Kong, China, pp. 524–529 (2006)Google Scholar

  • 5.

    Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: DMKD 2002, pp. 24–31 (2002)Google Scholar

  • 6.

    Kargupta, H., Das, K., Liu, K.: A game theoretic approach toward multiparty privacy-preserving distributed data mining. Technical Report TR-CS-0701, UMBC (April 2007)Google Scholar

  • 7.

    Schneier, B.: Applied Cryptography, 2nd edn. John Wiley & Sons, Chichester (1995)Google Scholar

  • 8.

    Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: ACM SIGKDD 2003, Washington, DC, pp. 206–215. ACM Press, New York (2003)Google Scholar


  • Advances in computing and communication over wired and wireless networks have resulted in many pervasive distributed computing environments. Many of these environments deal with different distributed sources of voluminous data, multiple compute nodes, and distributed user community. Analyzing and monitoring these distributed data sources require a data mining technology designed for distributed applications. The field of distributed data mining (DDM) deals with this problem---mining distributed data by paying careful attention to the distributed resources. The goal of this web site is to maintain and distribute a bibliography of DDM-related publications. We hope that DDM researchers and practitioners find this service useful. We welcome every help from the community in maintaining the bibliography and this web site.

    The bibliography is maintained and managed by Kanishka Bhaduri, Kamalika Das, Kun Liu and Prof. Hillol Kargupta.    

    You may add a single entry through our interactive web form or send us by email a bibtex file containing the entries. You can also take a view at those newly added entries which we will add to our database soon (provided they are relevant). If you submit an entry for the bibliography, we strongly encourage you to submit a pointer to the electronic version of the paper in case it is available online.

    DDM Bibliography is now grouped into separate categories.Please follow the link below to download the DDM Bibliography files.

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