A distributed data analytics framework, which involves a collection of distributed nodes that use their local data to create a local model, with an aggregator node creating a global model, which is shared across the nodes.
Published in Chapter:
Cybersecurity and Data Privacy in the Digital Age: Two Case Examples
Olakunle Olayinka (University of Sheffield, UK) and Thomas Win (University of West of England, UK)
Copyright: © 2022
|Pages: 15
DOI: 10.4018/978-1-7998-7712-7.ch007
Abstract
The COVID-19 pandemic has brought to the fore a number of issues regarding digital technologies, including a heightened focus on cybersecurity and data privacy. This chapter examines two aspects of this phenomenon. First, as businesses explore creative approaches to operate in the “new normal,” the security implications of the deployment of new technologies are often not considered, especially in small businesses, which often possess limited IT knowledge and resources. Second, issues relating to security and data privacy in monitoring the pandemic are examined, and different privacy-preserving data-sharing techniques, including federated learning, secure multiparty computation, and blockchain-based techniques, are assessed. A new privacy-preserving data-sharing framework, which addresses current limitations of these techniques, is then put forward and discussed. The chapter concludes that although the worst of the pandemic may soon be over, issues regarding cybersecurity will be with us for far longer and require vigilant management and the development of creative solutions.