An Effective Privacy Preservation Technique for Enhancing Data Usability

Year : 2025 | Volume : 12 | Issue : 03 | Page : 35 40
    By

    Dhananjay M. Kanade,

  • Shirish S. Sane,

  1. Research Scholar, Department of Computer Engineering, Karmaveer Kakasaheb Wagh Institute of Engineering Education and Research, Nashik, Savitribai Phule Pune University, Pune, Maharashtra, India
  2. Professor and Research Guide, Principal and Professor, Department of Computer Engineering, Gokhale Education Society’s R.H. Sapat College of Engineering, Management Studies and Research, Nashik, Maharashtra, India

Abstract

The rapid growth and adoption of modern database systems have created immense opportunities for researchers, industries, and organizations to extract meaningful knowledge and make data-driven decisions. While this progress has enabled the discovery of valuable patterns and trends, it has also intensified the challenge of safeguarding individual privacy. Merely removing direct identifiers such as names, social security numbers, or Aadhar card details is no longer sufficient, as adversaries can often exploit quasi-identifiers like gender, date of birth, and postal codes to re-identify individuals with alarming accuracy. To address these vulnerabilities, the field of Privacy-Preserving Data Publishing (PPDP) has emerged, offering techniques that attempt to strike a delicate balance between maintaining data utility and ensuring strong privacy guarantees. This study provides a detailed exploration of prominent PPDP models, including k-anonymity, ℓ-diversity, and t-closeness, while also reviewing newer strategies such as β-likeness and disassociation. Each method’s strengths, weaknesses, and real-world applicability are critically assessed. In addition, the study highlights the inherent trade-offs between data protection and usability, underlining the importance of adaptive, efficient, and context-aware solutions for secure data sharing in an era of growing privacy risks.

Keywords: Data anonymization, data utility, privacy-preserving data publishing (PPDP), general data protection regulation (GDPR)

[This article belongs to Recent Trends in Parallel Computing ]

How to cite this article:
Dhananjay M. Kanade, Shirish S. Sane. An Effective Privacy Preservation Technique for Enhancing Data Usability. Recent Trends in Parallel Computing. 2025; 12(03):35-40.
How to cite this URL:
Dhananjay M. Kanade, Shirish S. Sane. An Effective Privacy Preservation Technique for Enhancing Data Usability. Recent Trends in Parallel Computing. 2025; 12(03):35-40. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=232644


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 12/05/2025
Accepted 10/09/2025
Published 30/10/2025
Publication Time 171 Days
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