LDCML: A Novel AI-Driven Approach form Privacy-Preserving Anonymization of Quasi-Identifiers

Authors

  • Sreemoyee Biswas Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India Author
  • Vrashti Nagar Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India Author
  • Nilay Khare Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India Author
  • Priyank Jain Computer Science, Indian Institute of Information Technology, Pune, 412109, Maharashtra, India Author
  • Pragati Agrawal Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India Author

DOI:

https://doi.org/10.56294/dm2024287

Keywords:

Data Privacy, Data Analysis, Data Processing, L-Diversity, Machine Learning, Clustering Algorithms

Abstract

Introduction: the exponential growth of data generation has led to an escalating concern for data privacy on a global scale. This work introduces a pioneering approach to address the often overlooked data privacy leakages associated with quasi-identifiers, leveraging artificial intelligence, machine learning and data correlation analysis as foundational tools. Traditional data privacy measures predominantly focus on anonymizing sensitive attributes and exact identifiers, leaving quasi-identifiers in their raw form, potentially exposing privacy vulnerabilities.
Objective: the primary objective of the presented work, is to anonymise the quasi-identifiers to enhance the overall data privacy preservation with minimal data utility degradation.
Methods: In this study, the authors propose the integration of ℓ-diversity data privacy algorithms with the OPTICS clustering technique and data correlation analysis to anonymize the quasi-identifiers.
Results: to assess its efficacy, the proposed approach is rigorously compared against benchmark algorithms. The datasets used are - Adult dataset and Heart Disease Dataset from the UCI machine learning repository. The comparative metrics are - Relative Distance, Information Loss, KL Divergence and Execution Time.
Conclusion: the comparative performance evaluation of the proposed methodology demonstrates its superiority over established benchmark techniques, positioning it as a promising solution for the requisite data privacy-preserving model. Moreover, this analysis underscores the imperative of integrating artificial intelligence (AI) methodologies into data privacy paradigms, emphasizing the necessity of such approaches in contemporary research and application domains

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Published

2024-01-01

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Section

Original

How to Cite

1.
Biswas S, Vrashti Nagar VN, Khare N, Jain P, Agrawal P. LDCML: A Novel AI-Driven Approach form Privacy-Preserving Anonymization of Quasi-Identifiers. Data and Metadata [Internet]. 2024 Jan. 1 [cited 2024 Sep. 19];3:287. Available from: https://dm.ageditor.ar/index.php/dm/article/view/311