An Effective Privacy Preservation Technique for Enhancing Data Usability

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 03 | Page :
    By

    Dhananjay M Kanade,

  • Shirish S. Sane,

  1. Research Scholar, Department of Computer Engineering, K. K. Wagh Institute of Engineering Education and     Research, Nashik, Savitribai Phule Pune University, Pune, Maharashtra, India
  2. Professor and Research Guide, 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 paper 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 paper 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).

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):-.
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):-. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=232411


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Ahead of Print Subscription Review Article
Volume 12
03
Received 12/05/2025
Accepted 10/09/2025
Published 17/11/2025
Publication Time 189 Days
106

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