Big Data Privacy Context: Literature Effects On Secure Informational Assets

TL;DR

This study systematically reviews big data privacy literature, analyzing its effects on secure informational assets and identifying research gaps, especially in law and regulation, using bibliometrics, text mining, and multi-criteria decision analysis.

Contribution

It provides a comprehensive analysis of big data privacy research trends, identifies underexplored areas, and introduces ranking methods to evaluate literature effects on informational assets.

Findings

Focus on computational aspects in literature

Growing concerns on law and regulation

United States is the only country with consistent literature and SIS adoption

Abstract

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.