Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security 2021
DOI: 10.1145/3460120.3484565
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Locally Private Graph Neural Networks

Abstract: Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to preserve privacy while still allowing for effective learning over graph-structured datasets. However, achieving an ideal balance between accuracy and privacy in GNNs remains challenging due to the intrinsic structural connectivity of graphs. In this paper, we … Show more

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Cited by 55 publications
(35 citation statements)
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“…RELATED WORK DP on GNNs. In the past year or two, differentially private GNNs have become a very active research topic [23]- [30] (most of them are preprints). Most of them assume a centralized model [23]- [27] where the server has the entire graph (or the exact number of edges in the entire graph [25]).…”
Section: Our Contributions Our Contributions Are As Followsmentioning
confidence: 99%
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“…RELATED WORK DP on GNNs. In the past year or two, differentially private GNNs have become a very active research topic [23]- [30] (most of them are preprints). Most of them assume a centralized model [23]- [27] where the server has the entire graph (or the exact number of edges in the entire graph [25]).…”
Section: Our Contributions Our Contributions Are As Followsmentioning
confidence: 99%
“…Sajadmanesh and Gatica-Perez [30] apply LDP to each user's feature vectors in an attributed graph. However, they do not hide edges, which are sensitive in a social graph.…”
Section: Our Contributions Our Contributions Are As Followsmentioning
confidence: 99%
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