LPGNN: Graph Neural Networks with Local Differential Privacy
Description
The method combines Local Differential Privacy and Graph Neural Networks (GNNs) to preserve the privacy of node features and labels in GNNs, with relatively low privacy cost while providing competitive accuracy, using differential privacy principles.
Publications
Sajadmanes, S. and Gatica-Perez, D. (2021) Locally Private Graph Neural Networks. ACM Conference on Computer and Communications Security (CCS)
Links
- Repository: https://github.com/sisaman/LPGNN
Advantages
The method addresses the critically important issue of privacy as it applies to deep learning algorithms on graphs.
Applications
Applications involving graph data where nodes features need to be privacy-preserved.
Technology Readiness Level
TRL 3
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