(untitled) (International Journal of Engineering and Computer Science, 2024)
In the era of big data, the secure sharing of sensitive information across various domains such as healthcare, finance, and social networks has become increasingly vital. Traditional data anonymization techniques often struggle to balance the competing demands of preserving privacy and maintaining data utility, particularly in complex and dynamic data-sharing environments. This paper presents a novel hybrid approach to data anonymization that integrates differential privacy with adaptive anonymization algorithms, specifically designed to enhance privacy protection while retaining the analytical value of the data. The proposed methodology tailor’s anonymization strategies to the specific context of data sharing, effectively addressing the limitations of existing techniques. Extensive experiments conducted on diverse datasets, including healthcare and financial data, demonstrate the superior performance of this approach in reducing re-identification risks while maintaining high data utility. The findings suggest that these advancements in anonymization techniques provide a robust solution for secure and privacy-preserving data sharing, addressing the growing challenges posed by the increasing volume and sensitivity of data in modern digital ecosystems. The paper concludes with a discussion of the broader implications for cybersecurity and suggests future research directions to further enhance privacy-preserving technologies.