Problem
Research questions and friction points this paper is trying to address.
privacy leakage
edge-neighbouring inputs
graph generators
Empirical Auditing of Edge-Private Graph Generators
🤖 AI Summary
本文通过统计验证攻击方法，审计了图生成器的隐私泄露问题，比较了不同攻击方式在目标边周围几何结构上的效果。
📝 Abstract
We empirically audit privacy leakage by testing whether outputs from edge-neighbouring inputs remain distinguishable, using statistically valid lower bounds on the privacy loss witnessed by our attacks. Our framework compares direct-edge, local-structural, and GNN-based attacks through the geometry surrounding a target edge. Experiments across two generators and two networks show that privacy leakage is both mechanism- and network-dependent, with learned representations revealing information not captured by conventional local statistics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
edge-private graph generators
privacy leakage auditing
statistically valid lower bounds
GNN-based attacks
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