Privacy pentagon in Big Data Analytics
theoretical model proposal
DOI:
https://doi.org/10.21680/2447-0198.2024v8n1ID33898Keywords:
privacy, big data analytics, big data privacy, value of big data, privacy pentagonAbstract
We live in an environment characterized as an ocean of data, which grows not only in terms of volume and quantity, but also in terms of variety, being created and moving at high speed. Currently, structured data is much smaller in quantity and importance, and adjustments and improvements in technologies and analytical models were partly carried out to adapt to this new reality, which is known as Big Data Analytics. One of the issues of great concern in this new reality are threats to privacy. The question raised as a result of several research is that the procedures, techniques, technologies, and legislation currently available cannot fully guarantee privacy. Given this complex scenario, the objective of this research was to propose a multifaceted theoretical model within the scope of Big Data Analytics, which guarantees privacy, while not making its extraction of value unfeasible. The methodology proposed for this work was supported by the systematic literature review approach, with a view to critically analyzing the notes and conclusions of previous studies, identifying, and logically proposing new hypotheses and constructs, in order to format the final design of a theoric model. As a result, the Pentagon of Privacy in Big Data Analytics is proposed, which includes a kaleidoscope of solutions capable of guaranteeing privacy while guaranteeing the extraction of value in Big Data Analytics. The construct obtained as a result of this work provides a concise and consistent answer to the starting question of this work.
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