Saadat M Alhashmi

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.

The recent proliferation of ubiquitous computing technologies has led to the emergence of urban computing that aims to provide intelligent services to inhabitants of smart cities. Urban computing deals with enormous amounts of data collected from sensors and other sources in a smart city. In this article, we investigated and highlighted the role of urban computing in sustainable smart cities. In addition, a taxonomy was conceived that categorized the existing studies based on urban data, approaches, applications, enabling technologies, and implications. In this context, recent developments were elucidated. To cope with the engendered challenges of smart cities, we outlined some crucial use cases of urban computing. Furthermore, prominent use cases of urban computing in sustainable smart cities (e.g., planning in smart cities, the environment in smart cities, energy consumption in smart cities, transportation in smart cities, government policy in smart cities, and business processes in smart cities) for smart urbanization were also elaborated. Finally, several research challenges (such as cognitive cybersecurity, air quality, the data sparsity problem, data movement, 5G technologies, scaling via the analysis and harvesting of energy, and knowledge versus privacy) and their possible solutions in a new perspective were discussed explicitly.