Inuiguchi, Masahiro and Ichida, Hiroki and Torra, Vicenç (2019) Data anonymization with imprecise rules and its performance evaluations. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

Privacy protection is absolutely imperative for data releases when the utilization of public data and big data is getting popular. In this paper, data anonymization methods using rough set-based rule induction are investigated. It has been shown that many rules with imprecise conclusions can improve the classification accuracy of the rule-based classifier. Data anonymization methods utilizing rules with imprecise conclusions are proposed. The data tables anonymized by one of the proposed methods can preserve the classification accuracy of the rules induced from them. The proposed methods as well as conventional data anonymization methods are compared from two viewpoints: the classification accuracy of rules induced from the anonymized data table and the preservation of data anonymity. The results show the usefulness of the proposed methods.

Item Type:	Article
Additional Information:	Cite as: Inuiguchi, M., Ichida, H. & Torra, V. Data anonymization with imprecise rules and its performance evaluations. J Ambient Intell Human Comput (2019). https://doi.org/10.1007/s12652-019-01468-y
Keywords:	Rule induction; Imprecise rules; Privacy protection; Data anonymization;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14066
Identification Number:	https://doi.org/10.1007/s12652-019-01468-y
Depositing User:	Vicenç Torra
Date Deposited:	24 Feb 2021 15:11
Journal or Publication Title:	Journal of Ambient Intelligence and Humanized Computing
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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