PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Mueller, T.T.* ; Paetzold, J.C. ; Prabhakar, C.* ; Usynin, D.* ; Rueckert, D.* ; Kaissis, G.

Differentially private graph neural networks for whole-graph classification.

IEEE Trans. Pattern Anal. Mach. Intell. 45, 7308-7318 (2023)
Publ. Version/Full Text DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many machine learning applications such as the analysis of social and medical networks. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs. In this work, we introduce a framework for differential private graph-level classification. Our method is applicable to graph deep learning on multi-graph datasets and relies on differentially private stochastic gradient descent (DP-SGD). We show results on a variety of datasets and evaluate the impact of different GNN architectures and training hyperparameters on model performance for differentially private graph classification, as well as the scalability of the method on a large medical dataset. Our experiments show that DP-SGD can be applied to graph classification tasks with reasonable utility losses. Furthermore, we apply explainability techniques to assess whether similar representations are learned in the private and non-private settings. Our results can also function as robust baselines for future work in this area.
Impact Factor
Scopus SNIP
Altmetric
23.600
0.000
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Training; Privacy; Task analysis; Graph neural networks; Data models; Stochastic processes; Image edge detection; Differential privacy; graph neural networks; Database
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 0162-8828
e-ISSN 1939-3539
Journal IEEE Transactions on Pattern Analysis and Machine Intelligence
Quellenangaben Volume: 45, Issue: 6, Pages: 7308-7318 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1314 Usa
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-507100-001
G-505800-001
DOI 10.1109/TPAMI.2022.3228315
WOS ID 000982475600047
PubMed ID 37015371
Erfassungsdatum 2024-01-15
[➜Report correction]