Incremental privacy preservation for associative classification

Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but...

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Main Authors: Bowonsak Seisungsittisunti, Juggapong Natwichai
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/48926
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-489262018-08-16T02:08:32Z Incremental privacy preservation for associative classification Bowonsak Seisungsittisunti Juggapong Natwichai Business, Management and Accounting Decision Sciences Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known data privacy model, i.e. k-Anonymity. Meanwhile the data mining task to be applied to the given dataset is associative classification. As the problem of privacy preserving for data mining has proven as an NP-hard, we propose to study the characteristics of a proven heuristic algorithm in the incremental scenarios theoretically. Subsequently, we propose a few observations which lead to the techniques to reduce the computational complexity for the problem setting in which the outputs remains the same. In addition, we propose a simple algorithm, which is at most as efficient as the polynomial-time heuristic algorithm in the worst case, for the problem. Copyright 2009 ACM. 2018-08-16T02:06:46Z 2018-08-16T02:06:46Z 2009-12-01 Conference Proceeding 2-s2.0-74049135319 10.1145/1651449.1651458 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=74049135319&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/48926
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Business, Management and Accounting
Decision Sciences
spellingShingle Business, Management and Accounting
Decision Sciences
Bowonsak Seisungsittisunti
Juggapong Natwichai
Incremental privacy preservation for associative classification
description Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known data privacy model, i.e. k-Anonymity. Meanwhile the data mining task to be applied to the given dataset is associative classification. As the problem of privacy preserving for data mining has proven as an NP-hard, we propose to study the characteristics of a proven heuristic algorithm in the incremental scenarios theoretically. Subsequently, we propose a few observations which lead to the techniques to reduce the computational complexity for the problem setting in which the outputs remains the same. In addition, we propose a simple algorithm, which is at most as efficient as the polynomial-time heuristic algorithm in the worst case, for the problem. Copyright 2009 ACM.
format Conference Proceeding
author Bowonsak Seisungsittisunti
Juggapong Natwichai
author_facet Bowonsak Seisungsittisunti
Juggapong Natwichai
author_sort Bowonsak Seisungsittisunti
title Incremental privacy preservation for associative classification
title_short Incremental privacy preservation for associative classification
title_full Incremental privacy preservation for associative classification
title_fullStr Incremental privacy preservation for associative classification
title_full_unstemmed Incremental privacy preservation for associative classification
title_sort incremental privacy preservation for associative classification
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=74049135319&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48926
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