A COMPREHENSIVE REVIEW OF DATA ANONYMIZATION METHODS IN BIG DATA SYSTEMS

• Dr. Faisal Tariq

  Department of Data Science, National University of Sciences and Technology (NUST), Pakistan

The exponential growth of big data systems has made privacy concerns a significant challenge, especially in industries dealing with sensitive information such as healthcare, finance, and government sectors. Data anonymization, the process of removing personally identifiable information (PII) from data sets, is a critical technique for ensuring privacy in such environments. This comprehensive review aims to explore various data anonymization methods, their strengths, weaknesses, and applications in big data systems. The paper also examines the trade-off between data utility and privacy, offering a comparative analysis of popular anonymization techniques, such as k-anonymity, l-diversity, and differential privacy. The review discusses the latest advancements in the field and provides insights into emerging trends and future directions for research in data anonymization.

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