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

Sreemoyee Biswas, Vrashti Nagar Vrashti Nagar, Nilay Khare, Priyank Jain and Pragati Agrawal

Data and Metadata, 2024, vol. 3, 287

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

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:287:id:1056294dm2024287

DOI: 10.56294/dm2024287

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