A heuristic data reduction approach for associative classification rule hiding

Natwichai, J., Sun, X. and Xue, Li (2008). A heuristic data reduction approach for associative classification rule hiding. In: T-B. Ho and Z-H. Zhou, PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008: Tenth Pacific Rim International Conference on Artificial Intelligence, Hanoi, Vietnam, (140-151). 15-19 December 2008. doi:10.1007/978-3-540-89197-0_16


Author Natwichai, J.
Sun, X.
Xue, Li
Title of paper A heuristic data reduction approach for associative classification rule hiding
Conference name PRICAI 2008: Tenth Pacific Rim International Conference on Artificial Intelligence
Conference location Hanoi, Vietnam
Conference dates 15-19 December 2008
Proceedings title PRICAI 2008: Trends in Artificial Intelligence   Check publisher's open access policy
Journal name Pricai 2008: Trends in Artificial Intelligence   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer Verlag
Publication Year 2008
Sub-type Fully published paper
DOI 10.1007/978-3-540-89197-0_16
ISBN 978-3-540-89196-3
ISSN 0302-9743
1611-3349
Editor T-B. Ho
Z-H. Zhou
Volume 5351
Start page 140
End page 151
Total pages 12
Collection year 2009
Language eng
Abstract/Summary When data are to be shared between business partners, there could be some sensitive patterns which should not be disclosed to the other parties. On the other hand, the “quality” of the data must also be preserved. This creates an interesting question: how can we maintain the shared data that are guaranteed to have the quality, and the certain types of sensitive patterns be removed or “hidden”? In this paper, we address such the problem of sensitive classification rule hiding by using data reduction approach, i.e. removing the whole selected tuples in the given dataset. We focus on a specific type of classification rules, i.e. associative classification rules. In our context, a sensitive rule is hidden when its support falls below a minimal support threshold. Meanwhile, the impact on the data quality of the dataset is represented in term of a number of false-dropped rules, and a number of ghost rules. We present a few observations on the data quality with regard to the data reduction processes. From the observations, we can represent the impact by each reduction precisely without any re-applying the classification algorithm. Subsequently, we propose a heuristic algorithm to hide the sensitive rules based on the observations. Experimental results are presented to show the effectiveness and the efficiency of the proposed algorithm.
Subjects E1
890206 Internet Hosting Services (incl. Application Hosting Services)
080201 Analysis of Algorithms and Complexity
Q-Index Code E1
Q-Index Status Confirmed Code
Additional Notes Proceedings published in 'Lecture Notes in Computer Science' Book Series.

 
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Created: Fri, 17 Apr 2009, 08:58:14 EST by Ms Kimberley Nunes on behalf of School of Information Technol and Elec Engineering