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awesome-gnn-privacy

github.com/nds-vu/awesome-gnn-privacy ↗

This repository aims to provide links to works about privacy attacks and privacy preservation on graph data with Graph Neural Networks (GNNs).

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39

Curated Resources

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Categories

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2. GNN Privacy Attack Papers3. GNN Privacy Preservation Papers4. Datasets

Use this list with your AI agent

Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:

"Show me 2.1 membership inference attack resources from awesome-gnn-privacy"

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What's inside

4. Datasets

CC0 Universal

2. GNN Privacy Attack Papers

[paper]2.1 Membership Inference Attack
[paper]2.1 Membership Inference Attack
[paper]2.2 Link Inference Attack
[paper]2.2 Link Inference Attack
[paper]2.2 Link Inference Attack
[paper]2.2 Link Inference Attack

3. GNN Privacy Preservation Papers

[paper]3.1 Latent Factor Disentangling
[paper]3.1 Latent Factor Disentangling
[paper]3.1 Latent Factor Disentangling
[paper]3.1 Latent Factor Disentangling
[paper]3.2 Adversarial Training
[paper]3.2 Adversarial Training

Showing a sample of 39 resources. View the full list on GitHub →