Communication Dans Un Congrès Année : 2018

Graph-based Clustering under Differential Privacy

Résumé

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) 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.

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hal-02170699 , version 1 (02-07-2019)

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  • HAL Id : hal-02170699 , version 1

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Rafael Pinot, Anne Morvan, Florian Yger, Cedric Gouy-Pailler, Jamal Atif. Graph-based Clustering under Differential Privacy. Conference on Uncertainty in Artificial Intelligence (UAI 2018), Aug 2018, Monterey, California, United States. pp.329-338. ⟨hal-02170699⟩
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