Graph Structure Learning with Privacy Guarantees for Open Graph Data
📰 ArXiv cs.AI
A framework for graph structure learning with privacy guarantees for open graph data is proposed, using differential privacy to protect individual privacy
Action Steps
- Apply differential privacy to graph structure learning
- Enforce privacy guarantees at the data publishing stage rather than during model training
- Use the proposed framework to learn graph structures from open graph data while preserving individual privacy
Who Needs to Know This
Data scientists and AI engineers on a team can benefit from this framework to ensure privacy-preserving graph structure learning, while data publishers can use it to protect sensitive information in open graph data
Key Insight
💡 Differential privacy can be used to protect individual privacy in graph structure learning for open graph data
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🚀 Privacy-preserving graph structure learning for open graph data! 🤝
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