Graph-based Clustering under Differential Privacy

In this paper, we present the first differentially private clustering method for arbitraryshaped node clusters in a graph. This algorithm takes as input only an approximate Minimum Spanning Tree (MST) T released under weight differential privacy constraints from the graph. Then, the underlying nonconvex clustering partition is successfully recovered from cutting optimal cuts on T. As opposed to existing methods, our algorithm is theoretically well-motivated. Experiments support our theoretical findings.

Authors

• Rafael Pinot
• Anne Morvan
• Florian Yger
• Cédric Gouy-Pailler
• Jamal Atif

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