62 citations · 173 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
Nick Angelou, Ayoub Benaissa, Bogdan Cebere +9
We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-ba…
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…
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier
Adam James Hall, Nikolaos Pitropakis, William J Buchanan +1
Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists be…