arXiv:2606.04069v1 Announce Type: cross Abstract: Existing privacy analyses for Graph Neural Networks (GNNs) largely inherit assumptions from non-graph settings, overlooking structural correlations and stochastic training-graph sampling. In particular, node-dependent priors make type-I and type-II errors alone insufficient to characterize the best membership inference test. To address this, we introduce Bayesian Membership Privacy (BMP), a sampling-aware formulation of node-level membership privacy that incorporates node-dependent priors and treats graph sampling probabilities as part of the a
The increasing deployment of GNNs in sensitive applications necessitates more robust privacy guarantees, driving research into new privacy frameworks.
This work introduces a more nuanced understanding of privacy for GNNs, crucial for their ethical and secure deployment in areas like finance, healthcare, and social networks.
The proposed Bayesian Membership Privacy offers a new metric and methodology for evaluating and enhancing privacy in GNNs, moving beyond traditional assumptions.
- · AI developers
- · Data privacy researchers
- · Sectors using GNNs for sensitive data
- · Attackers attempting membership inference
- · Organizations with inadequate privacy frameworks
Improved privacy guarantees for Graph Neural Networks will increase their trustworthiness and adoption.
New privacy-preserving GNN architectures and training methods will emerge to comply with the Bayesian Membership Privacy framework.
This could lead to updated regulatory standards for AI models handling interconnected data, pushing for more sophisticated privacy assessments.
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Read at arXiv cs.LG