Graph-based Clustering under Differential Privacy
This addresses the need for privacy-preserving graph clustering, which is incremental as it builds on existing differential privacy methods but introduces a novel approach for this specific task.
The paper tackles the problem of clustering arbitrary-shaped node clusters in a graph under differential privacy constraints, achieving successful recovery of the underlying nonconvex clustering partition from an approximate Minimum Spanning Tree released with privacy.
In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph. This algorithm takes as input only an approximate Minimum Spanning Tree (MST) $\mathcal{T}$ released under weight differential privacy constraints from the graph. Then, the underlying nonconvex clustering partition is successfully recovered from cutting optimal cuts on $\mathcal{T}$. As opposed to existing methods, our algorithm is theoretically well-motivated. Experiments support our theoretical findings.