Graph Structure Learning with Privacy Guarantees for Open Graph Data
In the age of graph data – such as social networks, business relationship graphs, or knowledge maps – sharing these datasets for research or application purposes is increasingly common. But what if the structure of a graph itself contains sensitive information? Even without revealing the node contents, simply disclosing the existence of edges can lead to privacy breaches. Traditional approaches to Differential Privacy (DP) focus on protecting data during model training. In this paper, the authors go a step further — they aim to protect privacy at the moment of graph data publishing. They propose an elegant method based on Gaussian Differential Privacy (GDP) that enables learning the structure of a graph while maintaining strong privacy guarantees. ...