Bibliometric Analysis: Research Trends of Privacy in Big Data and its Applications

Personal private information is commonly available in the form of big data. Therefore, privacy concerns in big data and its applications are common issues. In this paper, we conduct a bibliometric analysis of privacy in big data and its applications. For example, in a financial management system, customers are exposed to vast amounts of financial information and interact with it, leading to privacy challenges as some customers may not adhere to the rules of financial institutions. A similar phenomenon occurs in healthcare, e-learning, and communication systems. Though there may be privacy breaches associated with these systems, the value of online services and the willingness of customers to use them are well-recognized. VOSviewer was used to develop scientific maps for bibliometric analysis. The current study includes the density map, network illustration, and overlay visualization. Data exported from the Dimensions database was utilized for bibliometric analysis. The themes that emerged related to privacy in big data and its applications include privacy, big data, security, blockchain, challenges, and the internet.