Privacy Models for Data Anonymization: a Comprehensive Comparative Analysis

EasyChair Preprint 14129

Abstract

This 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

@booklet{EasyChair:14129,
  author    = {Rizwana Rathmann},
  title     = {Privacy Models for Data Anonymization: a Comprehensive Comparative Analysis},
  howpublished = {EasyChair Preprint 14129},
  year      = {EasyChair, 2024}}