Aug 25, 2023 · We propose a principled privacy-preserving GNN framework that effectively safeguards both node and link privacy, referred to as dual-privacy preservation.
Oct 21, 2023 · This work takes a step back and investigates how GNNs contribute to privacy leakage. Through theoretical analysis and simulations, we identify ...
Oct 21, 2023 · ABSTRACT. Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their.
Jul 15, 2024 · This work takes a step back and investigates how GNNs contribute to privacy leakage. Through theoretical analysis and simulations, we identify ...
This work takes a step back and investigates how GNNs contribute to privacy leakage, and proposes a principled privacy-preserving GNN framework that ...
Request PDF | On Oct 21, 2023, Tianyi Zhao and others published Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs | Find, ...
Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs ... Graph Neural Networks (GNNs) are powerful tools for learning representations on ...
In this paper, we present Polarized message-passing (PMP), a novel paradigm to revolutionize the design of message-passing graph neural networks (GNNs).
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Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs ... Graph Neural Networks (GNNs) are powerful tools for learning representations on ...
To this end, we propose a privacy preserving GNNs framework, which not only protects the attribute privacy but also performs well in various downstream tasks.
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