Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/67744
Title: Privacy preservation for re-publication data by using probabilistic graph
Authors: Pachara Tinamas
Nattapon Harnsamut
Surapon Riyana
Juggapong Natwichai
Keywords: Computer Science
Engineering
Issue Date: 1-Jan-2019
Abstract: © Springer Nature Switzerland AG 2019. With the dynamism of data intensive applications, data can be changed by the insert, update, and delete operations, at all times. Thus, the privacy models are designed to protect the static dataset might not be able to cope with the case of the dynamic dataset effectively. m-invariance and m-distinct models are the well-known anonymization model which are proposed to protect the privacy data in the dynamic dataset. However, in their counting-based model, the privacy data of the target user could still be revealed on internally or fully updated datasets when they are analyzed using updated probability graph. In this paper, we propose a new privacy model for dynamic data publishing based on probability graph. Subsequently, in order to study the characteristics of the problem, we propose a brute-force algorithm to preserve the privacy and maintain the data quality. From the experiment results, our proposed model can guarantee the minimum probability of inferencing sensitive value.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082339981&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/67744
ISSN: 23674520
23674512
Appears in Collections:CMUL: Journal Articles

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