62 citations · 174 across the 7 of their papers we have counts for
7 papers
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…
Practical Defences Against Model Inversion Attacks for Split Neural Networks
Tom Titcombe, Adam J. Hall, Pavlos Papadopoulos +1
We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrat…
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…
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…