Associative classification rules hiding for privacy preservation
Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their 'quality'. In this paper, we address a problem of...
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th-cmuir.6653943832-498892018-09-04T04:19:47Z Associative classification rules hiding for privacy preservation Juggapong Natwichai Xingzhi Sun Xue Li Computer Science Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their 'quality'. In this paper, we address a problem of sensitive classification rule hiding by a data reduction approach. We focus on an important type of classification rules, i.e., associative classification rule. In our context, the impact on data quality generated by data reduction processes is represented by the number of false-dropped rules and ghost rules. To address the problem, we propose a few observations on the reduction approach. Subsequently, we propose a greedy algorithm for the problem based on the observations. Also, we apply two-bitmap indexes to improve the efficiency of the proposed algorithm. Experiment results are presented to show the effectiveness and the efficiency of the proposed algorithm. Copyright © 2011 Inderscience Enterprises Ltd. 2018-09-04T04:19:47Z 2018-09-04T04:19:47Z 2011-05-01 Journal 17515866 17515858 2-s2.0-79956073972 10.1504/IJIIDS.2011.040088 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79956073972&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49889 |
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Computer Science Juggapong Natwichai Xingzhi Sun Xue Li Associative classification rules hiding for privacy preservation |
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Sensitive patterns could be discovered from the given data when the data are shared between business partners. Such patterns should not be disclosed to the other parties. However, the shared data should be credible and trustworthy for their 'quality'. In this paper, we address a problem of sensitive classification rule hiding by a data reduction approach. We focus on an important type of classification rules, i.e., associative classification rule. In our context, the impact on data quality generated by data reduction processes is represented by the number of false-dropped rules and ghost rules. To address the problem, we propose a few observations on the reduction approach. Subsequently, we propose a greedy algorithm for the problem based on the observations. Also, we apply two-bitmap indexes to improve the efficiency of the proposed algorithm. Experiment results are presented to show the effectiveness and the efficiency of the proposed algorithm. Copyright © 2011 Inderscience Enterprises Ltd. |
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Juggapong Natwichai Xingzhi Sun Xue Li |
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Juggapong Natwichai Xingzhi Sun Xue Li |
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Juggapong Natwichai |
title |
Associative classification rules hiding for privacy preservation |
title_short |
Associative classification rules hiding for privacy preservation |
title_full |
Associative classification rules hiding for privacy preservation |
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Associative classification rules hiding for privacy preservation |
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Associative classification rules hiding for privacy preservation |
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associative classification rules hiding for privacy preservation |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79956073972&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/49889 |
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