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Title: Classification utility aware data stream anonymization
Authors: Sopaoğlu, Ugur
Abul, Osman
Keywords: Data streams
Data anonymization
Data privacy
Issue Date: 2021
Publisher: Elsevier
Abstract: Data streams are continuous, infinite and ordered sequences of data. In comparison to static dataset anonymization, data stream anonymization confront with a number of constraints and difficulties due to the dynamic nature of data flow. The literature already addressed the k-anonymization of data streams which contain quasi-identifier attributes. However, today most data streams contain sensitive and classification target attributes as well. This work's main motivation is to develop a k-anonymization method for data streams which additionally protects the sensitivity and enables effective classification models. The k-anonymization, as a result, is formulated as a weighted multi-objective optimization problem. There are three objectives with respective weights as user parameters. A clustering based k-anonymization algorithm is developed as the solution. An extensive experimental evaluation on three real datasets shows the effectiveness of our proposal in various configurations. Moreover, the experimental results also confirm that our proposal attains better classification accuracies in comparison to popular data stream anonymization techniques. (C) 2021 Elsevier B.V. All rights reserved.
ISSN: 1568-4946
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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