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ICDE
2009
IEEE

Deriving Private Information from Association Rule Mining Results

12 years 1 months ago
Deriving Private Information from Association Rule Mining Results
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 i...
Zutao Zhu, Guan Wang, Wenliang Du
Added 20 Oct 2009
Updated 20 Oct 2009
Type Conference
Year 2009
Where ICDE
Authors Zutao Zhu, Guan Wang, Wenliang Du
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