39 citations · 61 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
Can collaborative learning be private, robust and scalable?
Dmitrii Usynin, Helena Klause, Johannes C. Paetzold +2
In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the pri…
SoK: Differential Privacy on Graph-Structured Data
Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold +2
In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of gra…
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
Collaborative machine learning settings like federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion atta…
Differentially Private Graph Classification with GNNs
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar +3
Graph Neural Networks (GNNs) have established themselves as the state-of-the-art models for many machine learning applications such as the analysis of social networks, protein inte…