Performance Analysis of Proposed Scalable Reversible Randomization Algorithm (SRRA) in Privacy Preserving Big Data Analytics
Mohana Chelvan P, Veeramalai Natarajan Rajavarman, Dahlia Sam · International Journal of Advanced Computer Science and Applications · 2025
The economy of today’s world is a data-driven knowledge economy, as electronic devices are mostly used for our day-to-day activities, through which organizations collect data actively or passively. The dimensionality of the dataset is also increased, along with the volume of data, because of the advancements in digital devices and communication technology. The feature selection becomes a crucial preprocessing step in big data analytics as a dimensionality reduction technique to eliminate redundant and noisy features. Studying the fluctuations in feature selection results is a vigorous area of research, as it is positively related to data utility, as fluctuations in feature selection results confuse the data analysts’ minds about their research outcomes. Privacy preservation is a major concern in big data analytics to protect sensitive individuals’ data. Application of privacy preservation techniques to modify the dataset will affect the stability of feature selection, as it has recently been proven that it mostly depends on the dataset’s physical characteristics. This study analyses the performance of the proposed Scalable Reversible Randomization Algorithm (SRRA) in terms of privacy preservation, change in characteristics of the dataset, information loss, stability of feature selection, and data utility in big data scenarios.