International Journal of Science, Engineering and TechnologyAuthors: Dhananjay M.Kanade, Prof. Dr. Cherish S. Sane
Abstract: The exponential growth of data across healthcare, education, social networks, automotive systems, and cloud environments has intensified the need for robust and practical data anonymization strategies. This review synthesizes findings from multiple contemporary research works addressing anonymization frameworks, distributed anonymization, privacy–utility trade-offs, vulnerability analysis, clustering-based anonymization, diversity constraints, encryption-assisted anonymization, and novel methods including DNA-computing-based storage. The review identifies methodological advances, evaluates performance and scalability, and highlights challenges such as re-identification vulnerabilities, attribute sensitivity, bias propagation, and trade-offs between utility and privacy. The comparative analysis shows that while traditional techniques such as k-anonymity and l-diversity remain foundational, modern solutions integrate machine learning, distributed architectures, encryption, and clustering and mechanism design. Finally, the review outlines future research directions for developing context-aware, utility-optimized, and adversary-resistant anonymization systems suitable for heterogeneous and large-scale data ecosystems.
