An approximation algorithm for privacy preservation of associative classification

Privacy is one of the most important issues when the data are to be processed. Typically, given a dataset and a data processing goal, the privacy can be guaranteed by the pre-specified standard by applying privacy data-transformation algorithms. Furthermore, the utility of the dataset must be consid...

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Main Author: Juggapong Natwichai
Format: Conference Proceeding
Published: 2018
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954920947&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50715
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-507152018-09-04T04:45:51Z An approximation algorithm for privacy preservation of associative classification Juggapong Natwichai Computer Science Engineering Privacy is one of the most important issues when the data are to be processed. Typically, given a dataset and a data processing goal, the privacy can be guaranteed by the pre-specified standard by applying privacy data-transformation algorithms. Furthermore, the utility of the dataset must be considered while the transformation takes place. Such data transformation problem such that a privacy standard must be met and the utility must be optimized is an NP-hard problem. In this paper, we propose an approximation algorithm for the data transformation problem. The focused data processing addressed in this paper is classification using association rule, or associative classification. The proposed algorithm can transform the given datasets with O(k log k)-approximation utility comparing with the optimal solutions. The experiment results show that the algorithm can work effectively comparing with the optimal algorithm and the other heuristic algorithm. Also, the proposed algorithm is very efficient. 2018-09-04T04:44:39Z 2018-09-04T04:44:39Z 2010-07-30 Conference Proceeding 2-s2.0-77954920947 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954920947&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/50715
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Engineering
spellingShingle Computer Science
Engineering
Juggapong Natwichai
An approximation algorithm for privacy preservation of associative classification
description Privacy is one of the most important issues when the data are to be processed. Typically, given a dataset and a data processing goal, the privacy can be guaranteed by the pre-specified standard by applying privacy data-transformation algorithms. Furthermore, the utility of the dataset must be considered while the transformation takes place. Such data transformation problem such that a privacy standard must be met and the utility must be optimized is an NP-hard problem. In this paper, we propose an approximation algorithm for the data transformation problem. The focused data processing addressed in this paper is classification using association rule, or associative classification. The proposed algorithm can transform the given datasets with O(k log k)-approximation utility comparing with the optimal solutions. The experiment results show that the algorithm can work effectively comparing with the optimal algorithm and the other heuristic algorithm. Also, the proposed algorithm is very efficient.
format Conference Proceeding
author Juggapong Natwichai
author_facet Juggapong Natwichai
author_sort Juggapong Natwichai
title An approximation algorithm for privacy preservation of associative classification
title_short An approximation algorithm for privacy preservation of associative classification
title_full An approximation algorithm for privacy preservation of associative classification
title_fullStr An approximation algorithm for privacy preservation of associative classification
title_full_unstemmed An approximation algorithm for privacy preservation of associative classification
title_sort approximation algorithm for privacy preservation of associative classification
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77954920947&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50715
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