F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

Hasina Attaullah, Adeel Anjum, Tehsin Kanwal, Saif Ur Rehman Malik*, Alia Asheralieva, Hassan Malik, Ahmed Zoha, Kamran Arshad, Muhammad Ali Imran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 F-Classify 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.
Original languageEnglish
Pages (from-to)e4933
JournalSensors
Volume21
Issue number14
Early online date20 Jul 2021
DOIs
Publication statusE-pub ahead of print - 20 Jul 2021

Keywords

  • DCP
  • F-Classify
  • membership function
  • MSA
  • MST
  • (p, k) angelization
  • QT

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