186 citations · 239 across the 18 of their papers we have counts for
19 papers
Membership Inference Attacks Against Semantic Segmentation Models
Tomas Chobola, Dmitrii Usynin, Georgios Kaissis
Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this ca…
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…
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-…
Relationformer: A Unified Framework for Image-to-Graph Generation
Suprosanna Shit, Rajat Koner, Bastian Wittmann +8
A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blo…