47 citations · 182 across the 18 of their papers we have counts for
18 papers
Predicting Privacy Leakage from Weight Spectral Density
Richard J. Preen, Jim Smith
Membership inference attacks (MIAs) are widely used to audit the privacy disclosure risk of machine learning models, however current state-of-the-art attacks require training compu…
A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack
Richard J. Preen, Jim Smith
Machine learning models can inadvertently expose confidential properties of their training data, making them vulnerable to membership inference attacks (MIA). While numerous evalua…
A multi-language toolkit for the semi-automated checking of research outputs
Richard J. Preen, Maha Albashir, Simon Davy +1
This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. S…
Safe machine learning model release from Trusted Research Environments: The SACRO-ML package
Jim Smith, Richard J. Preen, Andrew McCarthy +8
We present SACRO-ML, an integrated suite of open source Python tools to facilitate the statistical disclosure control (SDC) of machine learning (ML) models trained on confidential…
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
Emily Jefferson, James Liley, Maeve Malone +16
TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure a…
Deep Learning with a Classifier System: Initial Results
Richard J. Preen, Larry Bull
This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks. Individual classifiers within…
Autoencoding with a Classifier System
Richard J. Preen, Stewart W. Wilson, Larry Bull
Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the…
Towards an Evolvable Cancer Treatment Simulator
Richard J. Preen, Larry Bull, Andrew Adamatzky
The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at…
Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening
Richard J. Preen, Jim Smith
Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computation…
Design Mining Microbial Fuel Cell Cascades
Richard J. Preen, Jiseon You, Larry Bull +1
Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it ha…
On Design Mining: Coevolution and Surrogate Models
Richard J. Preen, Larry Bull
Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototypi…
Design Mining Interacting Wind Turbines
Richard J. Preen, Larry Bull
An initial study of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan generated wind condit…
Toward the Coevolution of Novel Vertical-Axis Wind Turbines
Richard J. Preen, Larry Bull
The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy…
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Richard J. Preen, Larry Bull
Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but st…
Fuzzy Dynamical Genetic Programming in XCSF
Richard J. Preen, Larry Bull
A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Gen…
Discrete Dynamical Genetic Programming in XCS
Richard J. Preen, Larry Bull
A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results fro…
Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes
Richard J. Preen, Larry Bull
We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approxima…
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
Richard J. Preen, Larry Bull
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results fro…