Session

DescriptionThis minisymposium will bring together those working on enabling HPC facilities to be used effectively and efficiently when working with sensitive, confidential and personally identifiable data. Outside national security institutions, particularly in academic settings, HPC environments have traditionally been built with security standards appropriate to moderately sensitive data, in the physical sciences and engineering. Researchers are increasingly using large-scale computational methods to address questions in health and social science which require use of personally identifiable data obtained from governmental authorities. In order to maintain public consent for the use of such approaches “data saves lives” it is essential that an appropriate security environment is maintained. In these fields, secure environments are typically isolated from internet, have no in-out copy-paste facilities, a significant information governance overhead for actions as basic to the modern data scientist as pip install, and limited access to compute at scale needed for large digital twins or AI training and inference. Streaming data, continuously arriving from devices such as smart or personal equipment, is particularly challenging. Worldwide efforts are now focused on creating productive, secure environments called the Trusted Research Environments (TREs) at scale, which support the programming-language based approaches of modern data scientists and mathematical modellers.