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08/23/2023
Bias-Aware Minimisation: Understanding and Mitigating Estimator Bias in Private SGD
Differentially private SGD (DP-SGD) holds the promise of enabling the sa...
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07/13/2023
Explainable 2D Vision Models for 3D Medical Data
Training Artificial Intelligence (AI) models on three-dimensional image ...
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07/08/2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy
We explore Reconstruction Robustness (ReRo), which was recently proposed...
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02/03/2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in radiology
Artificial intelligence (AI) models are increasingly used in the medical...
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11/18/2022
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Differential privacy (DP) is typically formulated as a worst-case privac...
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11/08/2022
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
The prediction of pancreatic ductal adenocarcinoma therapy response is a...
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10/24/2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens
Differential Privacy (DP) provides tight upper bounds on the capabilitie...
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05/09/2022
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
The arguably most widely employed algorithm to train deep neural network...
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03/22/2022
Privacy: An axiomatic approach
The increasing prevalence of large-scale data collection in modern socie...
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03/01/2022
Differentially private training of residual networks with scale normalisation
We investigate the optimal choice of replacement layer for Batch Normali...
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12/21/2021
Distributed Machine Learning and the Semblance of Trust
The utilisation of large and diverse datasets for machine learning (ML) ...
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10/07/2021
Complex-valued deep learning with differential privacy
We present ζ-DP, an extension of differential privacy (DP) to complex-va...
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09/22/2021
Partial sensitivity analysis in differential privacy
Differential privacy (DP) allows the quantification of privacy loss when...
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09/22/2021
An automatic differentiation system for the age of differential privacy
We introduce Tritium, an automatic differentiation-based sensitivity ana...
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09/22/2021
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
The Gaussian mechanism (GM) represents a universally employed tool for a...
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07/30/2021
NeuralDP Differentially private neural networks by design
The application of differential privacy to the training of deep neural n...
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07/09/2021
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
We show that differentially private stochastic gradient descent (DP-SGD)...
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07/09/2021
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
In recent years, formal methods of privacy protection such as differenti...
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07/06/2021
Differentially private federated deep learning for multi-site medical image segmentation
Collaborative machine learning techniques such as federated learning (FL...
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12/10/2020
