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

Hasina Attaullah1, Adeel Anjum1,2, Tehsin Kanwal1

  • 1Department of Computer Sciences, COMSATS University, Islamabad 45550, Pakistan.

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|July 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces F-Classify, a novel fuzzy logic model for anonymizing sensitive health data. F-Classify effectively protects multiple sensitive attributes, improving privacy and data utility with faster processing.

Keywords:
(p, k) angelizationDCPF-ClassifyMSAMSTQTmembership function

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Area of Science:

  • Computer Science
  • Data Privacy
  • Artificial Intelligence

Background:

  • The rapid growth of data in smart environments necessitates robust privacy preservation techniques.
  • Anonymizing datasets with multiple sensitive attributes is challenging due to attribute correlations.
  • Existing methods often struggle with the complexity of multiple sensitive attributes.

Purpose of the Study:

  • To propose a novel fuzzy logic-based privacy model for anonymizing datasets with multiple sensitive attributes.
  • To address the unique challenges posed by attribute correlation in multi-attribute anonymization.
  • To enhance both data privacy and utility in the context of sensitive information.

Main Methods:

  • Development of the F-Classify model utilizing fuzzy logic for attribute classification.
  • Classification of quasi-identifier and multiple sensitive attributes based on defined rules.
  • Verification of the F-Classify algorithm's functionality using Hierarchical Label Propagation Network (HLPN).

Main Results:

  • F-Classify demonstrates superior performance in privacy and data utility compared to existing methods.
  • Experimental validation on healthcare datasets confirms the model's effectiveness.
  • The artificial intelligence-based approach offers a lower execution time.

Conclusions:

  • F-Classify provides an effective solution for privacy preservation in datasets with multiple sensitive attributes.
  • Fuzzy logic offers a promising approach for tackling complex data anonymization challenges.
  • The model balances privacy protection with data utility, making it suitable for real-world applications.