Data Quality in Privacy Preservation for Associative Classification

Harnsamut, N., Natwichai, J., Sun, X. and Li, X. (2008). Data Quality in Privacy Preservation for Associative Classification. In: Tang, C., Ling, C.X., Zhou, X, Cercone, N.J. and Xue, Li,, Advanced Data Mining and Applications. Fourth International Conference on Advanced Data Mining and Applications (ADAMA 2008), Chengu, China, (111-122). 8-10 Oct 2008. doi:10.1007/978-3-540-88192-6_12

Author Harnsamut, N.
Natwichai, J.
Sun, X.
Li, X.
Title of paper Data Quality in Privacy Preservation for Associative Classification
Conference name Fourth International Conference on Advanced Data Mining and Applications (ADAMA 2008)
Conference location Chengu, China
Conference dates 8-10 Oct 2008
Proceedings title Advanced Data Mining and Applications   Check publisher's open access policy
Place of Publication Berlin Heidelberg, Germany
Publisher Springer Verlag
Publication Year 2008
Sub-type Fully published paper
DOI 10.1007/978-3-540-88192-6_12
Open Access Status
ISBN 978-3-540-88191-9
ISSN 0302-9743
Editor Tang, C.
Ling, C.X.
Zhou, X
Cercone, N.J.
Xue, Li,
Start page 111
End page 122
Total pages 12
Language eng
Abstract/Summary Privacy preserving has become an essential process for any data mining task. In general, data transformation is needed to ensure privacy preservation. Once the privacy is preserved, data quality issue must be addressed, i.e. the impact on data quality should be minimized. In this paper, k-Anonymization is considered as the transformation approach for preserving data privacy. In such a context, we discuss the metrics of the data quality in terms of classification, which is one of the most important tasks in data mining. Since different type of classification may use different approach to deliver knowledge, data quality metric for the classification task should be tailored to a certain type of classification. Specifically, we propose a frequency-based data quality metric to represent the data quality of the transformed dataset in the situation that associative classification is to be processed. Subsequently, we validate our proposed metric with experiments. The experiment results have shown that our proposed metric can effectively reflect the data quality for the associative classification problem.
Subjects E1
890201 Application Software Packages (excl. Computer Games)
080201 Analysis of Algorithms and Complexity
Q-Index Code E1
Q-Index Status Confirmed Code

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Created: Fri, 17 Apr 2009, 19:28:47 EST by Ms Kimberley Nunes on behalf of School of Information Technol and Elec Engineering