Research map / Graph learning / GNN architectures
Metadata-conscious anonymous messaging
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adaptive diffusion (tree protocol) instead of standard diffusion | message spreading on d-regular trees vs spy-based adversary · d-regular trees, spy-based adversary | probability of detection | Helped |
| Use adaptive diffusion instead of standard diffusion on the Facebook graph | message spreading on real social graph · Facebook dataset, spy probability p≤0.15 | probability of detection | Helped |
| Increase tree degree d under standard diffusion | standard diffusion on d-regular trees · d-regular trees, spy-based adversary | probability of detection | Hurt |
| Increase tree degree d under adaptive diffusion | adaptive diffusion on d-regular trees · d-regular trees, spy-based adversary | probability of detection | Helped |
| Add snapshot observations to spy adversary (spy+snapshot) (ablation) | adaptive diffusion · regular trees, spy+snapshot adversary | probability of detection | No clear effect |
Closest papers by meaning
- Differentially Private Graph Diffusion with Applications in Personalized PageRanks (NeurIPS 2024 · similarity 0.83)
- DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans (NeurIPS 2024 · similarity 0.81)
- RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models (ICLR 2025 · similarity 0.81)
- An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization (ICLR 2024 · similarity 0.80)
- Backdoor Attack on Propagation-based Rumor Detectors (AAAI 2025 · similarity 0.80)
- InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy (NeurIPS 2025 · similarity 0.80)
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