Big Data Privacy in Multidimensional Domains: Models, Issues, Paradigms

This research focuses the attention on privacy-preserving big data analytics, which is an annoying problem in the modern big data era. Multidimensional big data analytics methods, which emphasize the use of multidimensional metaphors, have demonstrated notable success in OnLine Analytical Processing (OLAP) and Business Intelligence (BI) systems. Building on this foundation, privacy-preserving multidimensional big data analytics has emerged as a crucial research area, as it combines the inherent privacy concerns with advanced multidimensional analytics techniques. Within this conceptual context, in this paper, we provide a comprehensive overview that focuses the attention on privacy-preserving multidimensional big data analytics models, methods, and techniques over big data by highlighting several characteristics of the various in-literature proposals and also providing a review of future research challenges in this investigated research domain.