100 citations · 145 across the 24 of their papers we have counts for
7 papers · 1 filter
DP-GPL: Differentially Private Graph Prompt Learning
Jing Xu, Franziska Boenisch, Iyiola Emmanuel Olatunji +1
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by…
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Vincent Hanke, Tom Blanchard, Franziska Boenisch +3
While open Large Language Models (LLMs) have made significant progress, they still fall short of matching the performance of their closed, proprietary counterparts, making the latt…
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?
Iyiola E. Olatunji, Anmar Hizber, Oliver Sihlovec +1
Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown t…
Private Graph Extraction via Feature Explanations
Iyiola E. Olatunji, Mandeep Rathee, Thorben Funke +1
Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through…
Releasing Graph Neural Networks with Differential Privacy Guarantees
Iyiola E. Olatunji, Thorben Funke, Megha Khosla
With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of…
Achieving differential privacy for -nearest neighbors based outlier detection by data partitioning
Jens Rauch, Iyiola E. Olatunji, Megha Khosla
When applying outlier detection in settings where data is sensitive, mechanisms which guarantee the privacy of the underlying data are needed. The -nearest neighbors (-NN) al…
Membership Inference Attack on Graph Neural Networks
Iyiola E. Olatunji, Wolfgang Nejdl, Megha Khosla
Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focu…