Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining
Abd Elhameed, S., Abo-alian, A., Abdelkader, T., Badr, N. (2024). Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining. EKB Journal Management System, 24(4), 43-59. doi: 10.21608/ijicis.2025.332711.1361
Saad Abd Elhameed; Alshaimaa Abo-alian; Tamer Abdelkader; Nagwa Badr. "Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining". EKB Journal Management System, 24, 4, 2024, 43-59. doi: 10.21608/ijicis.2025.332711.1361
Abd Elhameed, S., Abo-alian, A., Abdelkader, T., Badr, N. (2024). 'Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining', EKB Journal Management System, 24(4), pp. 43-59. doi: 10.21608/ijicis.2025.332711.1361
Abd Elhameed, S., Abo-alian, A., Abdelkader, T., Badr, N. Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining. EKB Journal Management System, 2024; 24(4): 43-59. doi: 10.21608/ijicis.2025.332711.1361

Evaluating Privacy-Level Metrics in Privacy-Preserving Data Mining

International Journal of Intelligent Computing and Information Sciences
Volume 24, Issue 4, December 2024, Page 43-59  XML PDF (581.75 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2025.332711.1361
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Authors
Saad Abd Elhameed email orcid 1; Alshaimaa Abo-alianorcid 2; Tamer Abdelkaderorcid 3; Nagwa Badrorcid 2
1Planning Techniques Center, Institute of National Planning, Cairo, Egypt
2Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
3Dean, Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
Abstract
The increasing collection and analysis of sensitive personal data necessitates the development of robust Privacy-Preserving Data Mining (PPDM) methods. PPDM techniques are essential for extracting valuable insights from sensitive data while ensuring the maintenance of individuals’ privacy. A critical aspect of implementing PPDM involves assessing the efficacy of these techniques in safeguarding privacy. However, despite the growing significance of PPDM, there remains a limited comprehensive understanding of the metrics used to evaluate their effectiveness, particularly concerning privacy preservation. This paper addresses this research gap by presenting an extensive study of privacy-level metrics for PPDM methods. The study examines data privacy metrics, which quantify the uncertainty faced by adversaries attempting to infer original sensitive data from transformed datasets. In addition, the paper analyzes results privacy metrics, which assess the risk of sensitive information disclosure from data mining outputs. Besides, the paper presents a new classification for privacy-level metrics based on the phase of PPDM processes in which they can be utilized. Moreover, the study provides a detailed analytical discussion of privacy-level metrics used in PPDM, examining their strengths and limitations while demonstrating their implications for practical applications. Furthermore, the paper highlights several considerations and challenges associated with measuring privacy within different PPDM methods in the absence of a universally accepted definition. By providing a comprehensive overview of existing privacy-level metrics, the proposed study establishes a vital foundation for the evaluation of PPDM methods and contributes to the advancement of responsible and trustworthy data mining practices.
Keywords
Privacy-Preserving Data Mining; Privacy-level metrics; Data privacy; Results privacy; Quantifying privacy
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