44 citations · 70 across the 7 of their papers we have counts for
7 papers
PAN-DOMAIN: Privacy-preserving Sharing and Auditing of Infection Identifier Matching
William Abramson, William J. Buchanan, Sarwar Sayeed +2
The spread of COVID-19 has highlighted the need for a robust contact tracing infrastructure that enables infected individuals to have their contacts traced, and followed up with a…
Identifying Roles, Requirements and Responsibilities in Trustworthy AI Systems
Iain Barclay, Will Abramson
Artificial Intelligence (AI) systems are being deployed around the globe in critical fields such as healthcare and education. In some cases, expert practitioners in these domains a…
Syft 0.5: A Platform for Universally Deployable Structured Transparency
Adam James Hall, Madhava Jay, Tudor Cebere +20
We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. Th…
Privacy and Trust Redefined in Federated Machine Learning
Pavlos Papadopoulos, Will Abramson, Adam J. Hall +2
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare r…
Trust-by-Design: Evaluating Issues and Perceptions within Clinical Passporting
Will Abramson, Nicole E. van Deursen, William J Buchanan
A substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. Identity verification, pre-employment screening and app…
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Will Abramson, Adam James Hall, Pavlos Papadopoulos +2
When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust betwe…