Attaullah, Hasina and Anjum, Adeel and Kanwal, Tehsin and Malik, Saif Ur Rehman and Asheralieva, Alia and Malik, Hassan and Zoha, Ahmed and Arshad, Kamran and Imran, Muhammad Ali (2021) F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors, 21 (14). p. 4933. DOI https://doi.org/10.3390/s21144933
Attaullah, Hasina and Anjum, Adeel and Kanwal, Tehsin and Malik, Saif Ur Rehman and Asheralieva, Alia and Malik, Hassan and Zoha, Ahmed and Arshad, Kamran and Imran, Muhammad Ali (2021) F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors, 21 (14). p. 4933. DOI https://doi.org/10.3390/s21144933
Attaullah, Hasina and Anjum, Adeel and Kanwal, Tehsin and Malik, Saif Ur Rehman and Asheralieva, Alia and Malik, Hassan and Zoha, Ahmed and Arshad, Kamran and Imran, Muhammad Ali (2021) F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors, 21 (14). p. 4933. DOI https://doi.org/10.3390/s21144933
Abstract
With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model <i>F-Classify</i> that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.
Item Type: | Article |
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Uncontrolled Keywords: | Algorithms; Artificial Intelligence; Fuzzy Logic; Models, Theoretical; Privacy; DCP; F-Classify; membership function; MSA; MST; (p, k) angelization; QT |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Sep 2024 14:24 |
Last Modified: | 30 Oct 2024 21:34 |
URI: | http://repository.essex.ac.uk/id/eprint/37347 |
Available files
Filename: F-Classify Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. .pdf
Licence: Creative Commons: Attribution 4.0