Application of Bat Algorithm for Data Anonymization

Abstract

The rapid proliferation of digital data has raised significant concerns regarding privacy and data security, necessitating the development of effective data anonymization techniques. This research presents a novel application of the Bat Algorithm (BA) for data anonymization, a nature-inspired optimization algorithm that mimics the echolocation behavior of Bats. The proposed approach leverages the BA's unique search capabilities to achieve a delicate balance between data utility and privacy preservation, a critical aspect in today's data-driven world. By treating data attributes as potential solutions and employing the BA's search process, the algorithm iteratively identifies and modifies sensitive attributes while minimizing information loss. This research contributes to the developing field of research on data anonymization by introducing a nature-inspired optimization technique that offers a promising alternative to traditional anonymization methods. Experimental results on various real-world datasets demonstrate the effectiveness of the proposed approach in achieving robust privacy protection while maintaining data quality, outperforming existing anonymization methods in terms of utility and computational efficiency. Furthermore, the proposed BA-based data anonymization approach exhibits versatility, scalability, and adaptability, making it suitable for diverse application domains, from healthcare and finance to social media and beyond. In summary, this study highlights the potential of the Bat Algorithm as a valuable tool in the field of data anonymization, offering a promising avenue for addressing the privacy challenges associated with the ever-expanding digital data landscape.