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Mueller, T.T.* ; Usynin, D.* ; Paetzold, J.C. ; Braren, R.* ; Rueckert, D.* ; Kaissis, G.

Differential privacy guarantees for analytics and machine learning on graphs: A survey of results.

J. Priv. Confid. 14, DOI: 10.29012/jpc.820 (2024)
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We study differential privacy (DP) in the context of graph-structured data and discuss its formulations and applications to the publication of graphs and their associated statistics, graph generation methods, and machine learning on graph-based data, including graph neural networks (GNNs). Interpreting DP guarantees in the context of graphstructured data can be challenging, as individual data points are interconnected (often non-linearly or sparsely). This differentiates graph databases from tabular databases, which are usually used in DP, and complicates related concepts like the derivation of per-sample gradients in GNNs. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, and discuss challenges and promising applications, including the GNN domain. We compare and separate works into methods that privately estimate graph data (either by statistical analysis or using GNNs), and methods that aim at generating new graph data. We conclude our work with a discussion of open questions and potential directions for further research in this area.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Differential Privacy ; Graph Analytics ; Graph Neural Networks ; Graph-structured Data
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2575-8527
e-ISSN 2575-8527
Zeitschrift Journal of Privacy and Confidentiality
Quellenangaben Band: 14, Heft: 1 Seiten: , Artikelnummer: , Supplement: ,
Verlag Cornell University Library
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
G-505800-001
DOI 10.29012/jpc.820
Scopus ID 85185316952
Erfassungsdatum 2024-04-26
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