EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
arXiv:2607.08659v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-level differential privacy, a common approach is to inject noise into all elements of the graph's adjacency matrix, thereby obfuscating the existence of any single edge. However, stronger privacy requires more noise, and excessive noise reduces utility, making the privacy-utility balance a major barrier to practic
The proliferation of GNNs in sensitive domains creates an urgent need for robust privacy mechanisms, making research into privacy-utility balance critical.
Achieving both high utility and strong differential privacy in graph data is crucial for the widespread adoption of GNNs in privacy-sensitive applications like healthcare and finance.
This research outlines a method to better balance privacy and utility in GNNs, potentially accelerating their deployment in regulated industries.
- · AI developers
- · Healthcare sector
- · Financial services
- · Privacy-focused businesses
- · Organizations with poor data governance
- · Bad actors exploiting data leaks
Improved privacy guarantees for GNN applications, leading to higher trust and adoption.
New regulatory frameworks may emerge, setting standards for private AI systems.
Enhanced privacy could unlock new forms of data sharing and collaboration within competitive or sensitive industries.
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Read at arXiv cs.LG