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Recent Trends in Parallel Computing Review Article

An Effective Privacy Preservation Technique for Enhancing Data Usability

  1. Dhananjay M. Kanade Department of Computer Engineering, Karmaveer Kakasaheb Wagh Institute of Engineering Education and Research, Nashik, Savitribai Phule Pune University, Pune
  2. Shirish S. Sane Principal and Professor, Department of Computer Engineering, Gokhale Education Society's R.H. Sapat College of Engineering, Management Studies and Research, Nashik

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

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