Evaluating Data Anonymization Techniques for Privacy and Security

Abstract

Collecting, processing, and analysing vast quantities of data is ongoing in today's data-driven society. While this mountain of data may provide useful insights, it also poses serious privacy risks. Unauthorized access and exploitation of sensitive personal data constitute a significant threat to people' privacy. Data anonymization has become an important tactic for addressing these issues since it protects privacy without compromising the ability to utilize data for analytics. Data anonymization methods safeguard individuals' privacy by converting PII into non-identifiable information. It may be difficult to apply these methods to large-scale data analytics, and their efficacy varies. An important consideration is how to protect individuals' privacy without compromising the data's utility for analysis, as anonymised data may no longer be as insightful. This research delves into several approaches to data anonymization and assesses their pros and cons within the framework of large amounts of data analytics. Additionally, it delves into the tension between data privacy and data usefulness, drawing attention to the need of strong anonymization frameworks capable of managing the ever-increasing size and complexity of contemporary data sets. As a last point, it stresses the need for further study into better anonymization methods to safeguard individuals' privacy without sacrificing big data's ability to spur innovation and discovery.