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LDCML: a novel ai-driven approach for privacy-preserving anonymization of quasi-identifiers

By
Sreemoyee Biswas ,
Sreemoyee Biswas

Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

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Vrashti Nagar ,
Vrashti Nagar

Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

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Nilay Khare ,
Nilay Khare

Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

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Priyank Jain ,
Priyank Jain

Computer Science, Indian Institute of Information Technology, Pune, 412109, Maharashtra, India

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Pragati Agrawal ,
Pragati Agrawal

Computer Science, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

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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.

How to Cite

1.
Biswas S, Nagar V, Khare N, Jain P, Agrawal P. LDCML: a novel ai-driven approach for privacy-preserving anonymization of quasi-identifiers. Data and Metadata [Internet]. 2024 May 17 [cited 2024 Jun. 24];3:287. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/287

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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