Publications (5)
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…
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…
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…
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
Daniele Romanini, Adam James Hall, Pavlos Papadopoulos +6
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on…
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…