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Vaccarino, A.L., Dharsee, M., Strother, S., Aldridge, D., Arnott, S.R., Behan, B., Dafnas, C., Dong, F., Edgecombe, K., El-Badrawi, R., El-Emam, K., Gee, T., Evans, S.G., Javadi, M., Jeanson, F., Lefaivre, S., Lutz, K., MacPhee, F.C., Mikkelsen, J., Mikkelsen, T., Mirotchnick, N., Schmah, T., Studzinski, C.M., Stuss, D.T., Theriault, E. and Evans, K.R. (2018) Brain-Code: A Secure Neuroinformatics Platform for Management, Federation, Sharing and Analysis of Multi-Dimensional Neuroscience Data. Frontiers in Neuroinformatics, 12, Article No. 28.
https://doi.org/10.3389/fninf.2018.00028

has been cited by the following article:

• TITLE: Addressing the Security Challenges of Big Data Analytics in Healthcare Research

  AUTHORS: Mohamed Sami Rakha, Lucas Lapczyk, Costa Dafnas, Patrick Martin

  KEYWORDS: Big Data, Analytics Pipeline, Security, Data Safe Haven, CIMVHR, Health Data, Data Repository, Restricted Data Environment

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.15 No.8, August 3, 2022

  ABSTRACT: Big data and associated analytics have the potential to revolutionize healthcare through the tools and techniques they offer to manage and exploit the large volumes of heterogeneous data being collected in the healthcare domain. The strict security and privacy constraints on this data, however, pose a major obstacle to the successful use of these tools and techniques. The paper first describes the security challenges associated with big data analytics in healthcare research from a unique perspective based on the big data analytics pipeline. The paper then examines the use of data safe havens as an approach to addressing the security challenges and argues for the approach by providing a detailed introduction to the security mechanisms implemented in a novel data safe haven. The CIMVHR Data Safe Haven (CDSH) was developed to support research into the health and well-being of Canadian military, Veterans, and their families. The CDSH is shown to overcome the security challenges presented in the different stages of the big data analytics pipeline.