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
Share This
🚀 Privacy-preserving graph structure learning for open graph data! 🤝
Key Takeaways
A framework for graph structure learning with privacy guarantees for open graph data is proposed, using differential privacy to protect individual privacy
Full Article
Title: Graph Structure Learning with Privacy Guarantees for Open Graph Data
Abstract:
arXiv:2507.19116v3 Announce Type: replace-cross Abstract: Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) provides rigorous guarantees, most existing approaches enforce privacy during model training rather than at the data publishing stage. This limits the applicability to open-data scenarios. We propose a privacy-preserving graph structure learning framework that inte
Abstract:
arXiv:2507.19116v3 Announce Type: replace-cross Abstract: Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) provides rigorous guarantees, most existing approaches enforce privacy during model training rather than at the data publishing stage. This limits the applicability to open-data scenarios. We propose a privacy-preserving graph structure learning framework that inte
DeepCamp AI