Deriving private information from association rule mining results

Zutao Zhu, Guan Wang, Wenliang Du

Research output: Chapter in Book/Entry/PoemConference contribution

15 Scopus citations


Data publishing can provide enormous benefits to the society. However, due to privacy concerns, data cannot be published in their original forms. Two types of data publishing can address the privacy issue: one is to publish the sanitized version of the original data, and the other is to publish the aggregate information from the original data, such as data mining results. There have been extensive studies to understand the privacy consequence in the first approach, but there is not much investigation on the privacy consequence of publishing data mining results, although, it is well believed that publishing data mining results can lead to the disclosure of private information. We propose a systematic method to study the privacy consequence of data mining results. Based on a well-established theory, the principle of maximum entropy, we have developed a method to precisely quantify the privacy risk when data mining results are published.We take the association rule mining as an example in this paper, and demonstrate how we quantify the privacy risk based on the published association rules. We have conducted experiments to evaluate the effectiveness and performance of our method.We have drawn several interesting observations from our experiments.

Original languageEnglish (US)
Title of host publicationProceedings - 25th IEEE International Conference on Data Engineering, ICDE 2009
Number of pages12
StatePublished - 2009
Event25th IEEE International Conference on Data Engineering, ICDE 2009 - Shanghai, China
Duration: Mar 29 2009Apr 2 2009

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other25th IEEE International Conference on Data Engineering, ICDE 2009

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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