Download PDFOpen PDF in browserPrivacy Models for Data Anonymization: a Comprehensive Comparative AnalysisEasyChair Preprint 141297 pages•Date: July 25, 2024AbstractThis paper provides an in-depth discussion of existing anonymization privacy models. The main focus is on their applications, strengths, and limitations, with a particular emphasis on k-anonymity. The paper explores the theoretical foundations of k-anonymity and its extensions, such as l-diversity and t-closeness. It analyzes how these models contribute to safeguarding individual privacy in data publishing. This paper comprehensively reviews current methodologies and highlights the practical implementations of k-anonymity in various domains, including healthcare, finance, and social sciences. Case studies and experimental results from real-world data sets demonstrate the effectiveness and challenges of applying k-anonymity in different scenarios. Keyphrases: Big Data, Privacy Models, achieving k anonymity, differential privacy, k-anonymity, l-diversity
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