Disguise Guard: Annonymizing the Data to Enhance Privacy

In today's data-driven world, the growth of data sharing has prompted considerable issues over privacy protection. Anonymization techniques play a significant role in minimising these issues by safeguarding people' privacy while enabling valuable data analysis and exchange. This research analyses several anonymization strategies and their consequences for privacy preservation in data sharing settings. We discuss the issues associated with balancing privacy and utility and review state-of-the-art strategies for obtaining anonymity, such as k-anonymity, l-diversity, and t-closeness. Additionally, we analyse the limitations and trade-offs of these strategies and propose future research avenues to address rising privacy challenges in the context of developing data environments.