activity
20202025
most citedDifferentially Private Graph Classification with GNNs

39 citations · 61 across the 19 of their papers we have counts for

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Showing cs.CRShow all

7 papers · 1 filter

cs.CR2025

Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models

Asia Belfiore, Jonathan Passerat-Palmbach, Dmitrii Usynin

The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explore…

cs.CR2022★ 1 cited

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…

cs.CR2022

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…

cs.CR2022★ 6 cited

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…

cs.CR2021

A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index

Georgios Kaissis, Moritz Knolle, Friederike Jungmann +3

The Gaussian mechanism (GM) represents a universally employed tool for achieving differential privacy (DP), and a large body of work has been devoted to its analysis. We argue that…

cs.CR2021

Partial sensitivity analysis in differential privacy

Tamara T. Mueller, Alexander Ziller, Dmitrii Usynin +4

Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the pro…

cs.CR2020★ 7 cited

Privacy-preserving medical image analysis

Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel +8

The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy pr…