42 citations · 80 across the 28 of their papers we have counts for
6 papers · 1 filter
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
Tamara T. Mueller, Stefan Kolek, Friederike Jungmann +5
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their at…
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients
Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann +3
The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity. The training of neural netw…
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens
Georgios Kaissis, Alexander Ziller, Stefan Kolek Martinez de Azagra +1
Differential Privacy (DP) provides tight upper bounds on the capabilities of optimal adversaries, but such adversaries are rarely encountered in practice. Under the hypothesis test…
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
Nicolas W. Remerscheid, Alexander Ziller, Daniel Rueckert +1
The arguably most widely employed algorithm to train deep neural networks with Differential Privacy is DPSGD, which requires clipping and noising of per-sample gradients. This intr…
Privacy: An axiomatic approach
Alexander Ziller, Tamara Mueller, Rickmer Braren +2
The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-…
Differentially private training of residual networks with scale normalisation
Helena Klause, Alexander Ziller, Daniel Rueckert +2
The training of neural networks with Differentially Private Stochastic Gradient Descent offers formal Differential Privacy guarantees but introduces accuracy trade-offs. In this wo…