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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).
https://github.com/NDS-VU/awesome-gnn-privacy
Last synced: 3 days ago
JSON representation
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3. GNN Privacy Preservation Papers
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3.4 Federated Learning Approaches
- [paper
- [paper
- [paper
- [paper
- [paper
- [paper - yao/FedGCN)
- [paper
- [paper
- [paper
- [paper - whitelab/fedchem)
- [paper
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- [paper - AI/FedML/tree/master/python/app/fedgraphnn)
- [paper
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- [paper
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- [paper
- [paper
- [paper
- [paper - AI/SpreadGNN)
- [paper
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3.3 Differentially Private Approaches
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3.1 Latent Factor Disentangling
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3.2 Adversarial Training
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2. GNN Privacy Attack Papers
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4. Datasets
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3.4 Federated Learning Approaches
- Facebook - Twitter.html), [LastFM](https://snap.stanford.edu/data/feather-lastfm-social.html), [Reddit](https://paperswithcode.com/dataset/reddit), [Computers](https://docs.dgl.ai/en/0.9.x/generated/dgl.data.AmazonCoBuyComputerDataset.html)
- Cora - arxiv](https://ogb.stanford.edu/docs/nodeprop), [Aminer](https://www.aminer.org/data/)
- Flixster
- NCI1 - 8H](https://remap2022.univ-amu.fr/biotype_page/OVCAR-8:9606)
- PROTEINS
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