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arXiv · 1803.03831

Graph-based Clustering under Differential Privacy

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

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.

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Rafael Pinot, Anne Morvan, Florian Yger, Cédric Gouy-Pailler, Jamal Atif. 2018-03-10. Graph-based Clustering under Differential Privacy. https://arxiv.org/abs/1803.03831

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