Privacy-preserving multidimensional big data analytics models, methods and techniques: A comprehensive survey

The topic of privacy-preserving big data analytics is gaining momentum now, thanks to the plethora of modern application scenarios where it can be successfully applied. These range from smart city applications to e -government, from social networks to graph-like analysis tools, from logistic transport systems to business intelligence systems, and so forth. Healthcare systems , for instance, are turning out to be one of the major applicative settings where privacy-preserving big data analytics tools are extensively applied, due to the specific requirements dictated by the context (i.e., protecting individual privacy). Multidimensional big data analytics methods are a special case of big data analytics methods where the emphasis is put on the usage of multidimensional metaphors , which had a fortunate experience in OnLine Analytical Processing and Business Intelligence systems. One step forward, privacy-preserving multidimensional big data analytics , is thus of extreme interest to the research community because it combines the intrinsic privacy-preserving problem with the emerging multidimensional big data analytics methods. Inspired by these considerations, 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 the investigated research domain. • We focus on privacy-preserving multidimensional big data analytics analysis. • We define a set and various methods and techniques for classifying literature. • Classification and categorization of relevant literature research works is defined. • We summarize the selected references in a table, based on their properties. • We illustrate a review of future research challenges in this investigated domain.