Notice of Violation of IEEE Publication PrinciplesA Novel Method for Protecting Sensitive Knowledge in Association Rules Mining

Notice of Violation of IEEE Publication Principles

"A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining"
in the 2006 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA\´06)

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper contains significant duplication of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following two articles:

"A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining"
in the 2005 Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSACC\´05)

and

"A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining" by En Tzu Wang
in a Masters thesis for Dept. Of Computer Science and Information Engineering, National Dong Hwa University, Taiwan, June 2005

http://etd.lib.ndhu.edu.tw/theses//available/etd-0716105-171246/unrestricted/etd-0716105-171246.pdfIn the researches of data mining, discovering frequent patterns from huge amounts of data is one of the most studied problems. The frequent patterns mined from databases can bring the users many commercial benefits. However, some sensitive patterns with security concerned may cause a threat to privacy. It investigates to find an appropriate balance between a need for privacy and information discovery on frequent patterns. It proposes a novel method for modifying databases to hide sensitive patterns. By multiplying the original da- abase and a sanitization matrix together, a sanitized database with privacy concerns is obtained. Additionally, two probability policies are introduced to against the recovery of sensitive patterns and reduce the probability of hiding non-sensitive patterns in the sanitized database. The complexity analysis of our sanitization process is proved and a set of experiments is also performed to show the benefit of our approach