Modified Algorithm for Privacy Preservation of Gathered Data

Abstract

The extensive prevalence of computing technologies in our daily lives and physical landscapes has led to the generation of vast datasets for analysis. Although, there is a growing uncertaintyregarding potential privacy breaches, as sensitive data could be exposed if not adequately protected during analysis. Many existing privacy-preserving methods encounter challenges such as inefficiency, scalability limitations, and the delicate balance between data utility and privacy conservation.

In this study, we introduce an algorithm known as PABIDOT. PABIDOT employs optimal geometric transformations to safeguard privacy within the realm of big data. We assess the efficacy of PABIDOT through a series of experiments involving nine distinct datasets and five classification algorithms. Our results highlight PABIDOT's exceptional execution speed, scalability, resilience against potential attacks,andits accuracy in large-scale datasets.In addition, we delve into the practical implications of PABIDOT in real-world scenarios, emphasizing its adaptability across diverse industries and applications. The algorithm's robustness in safeguarding sensitive information while maintaining data integrity sets a new standard in privacy preservation. Moreover, the study sheds light on potential avenues for further advancements in the field of secure data analytics, paving the way for more comprehensive and effective privacy solutions in the era of burgeoning data generation and utilization.