A privacy-aware service-oriented platform for distributed data mining

Xiaofeng Zhang*, Ho Fai Wong, Kwok Wai CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)


Customer data privacy is known to be a factor which makes just-in-time data sharing and mining among enterprises challenging. Learning-from-abstraction is a recently proposed paradigm for privacy preserving distributed data mining where distributed local data sources are protected by probabilistic data abstraction. In this paper, we investigate the use of a normalized negative log likelihood together with the paradigm for quantifying the level of privacy protection, and studied theoretically the change of the privacy levels of the local data abstractions after being aggregated for global data analysis. Experiments on distributed data clustering with a synthetic data set were conducted on a service-oriented BPEL platform. The promising results obtained demonstrates the effectiveness of the adopted privacy measure.

Original languageEnglish
Title of host publicationProceedings - CEC/EEE 2006
Subtitle of host publicationJoint Conference - 8th IEEE International Conference on E-Commerce and Technology (CEC 2006), 3rd IEEE International Conference on Enterprise Computing, E-Commerce
PublisherIEEE Computer Society
Number of pages4
ISBN (Print)0769525113, 9780769525112
Publication statusPublished - 2006
EventCEC/EEE 2006 Joint Conferences - San Francisco, CA, United States
Duration: 26 Jun 200629 Jun 2006

Publication series

NameCEC/EEE 2006 Joint Conferences


ConferenceCEC/EEE 2006 Joint Conferences
Country/TerritoryUnited States
CitySan Francisco, CA

Scopus Subject Areas

  • Engineering(all)


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