Research Article
An Effective Clustering Based Privacy Preserving Model Against Feature Attacks
Muhammad Zulqurnain*
,
Muazzam Ali Khan Khattak,
Adeel Anjum,
Tehsin Kanwal
Issue:
Volume 14, Issue 3, June 2025
Pages:
44-59
Received:
21 March 2025
Accepted:
19 April 2025
Published:
19 June 2025
DOI:
10.11648/j.ijiis.20251403.11
Downloads:
Views:
Abstract: The rise in healthcare-related illnesses has generated a substantial amount of patient data, making the safeguarding of patient data imperative. Existing privacy protection methods face challenges, including longer execution times, compromised data quality, and increased information loss as data dimensions expand. Effective attribute selection is vital to enhance preservation methods. Our research introduces a privacy-preserving clustering approach that addresses these concerns through two stages: feature selection and anonymization. The first stage selects relevant features using symmetrical uncertainty (SU) and eliminates duplicates with Kendall’s Tau Correlation Coefficient. The Utility Preserved Anonymization (UPA) algorithm is employed in the second phase to achieve privacy preservation. Additionally, our approach reduces data dimensionality to simplify cluster creation for anonymization. Experimental analysis on real-time data demonstrates the strategy’s effectiveness, with outstanding sensitivity (97.85%) and accuracy (95%), efficiently eliminating unnecessary features and simplifying clustering complexity.
Abstract: The rise in healthcare-related illnesses has generated a substantial amount of patient data, making the safeguarding of patient data imperative. Existing privacy protection methods face challenges, including longer execution times, compromised data quality, and increased information loss as data dimensions expand. Effective attribute selection is v...
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