Thumbnail

Graph-based Clustering under Differential Privacy

Pinot, Rafael; Morvan, Anne; Yger, Florian; Gouy-Pailler, Cédric; Atif, Jamal (2018), Graph-based Clustering under Differential Privacy, in Globerson, Amir; Silva, Ricardo, Uncertainty in Artificial Intelligence (UAI) - Proceedings of the Thirty-Fourth Conference (2018), AUAI Press : Corvallis (Oregon, USA), p. 329-338

View/Open
Type
Communication / Conférence
Date
2018
Conference title
Conference on Uncertainty in Artificial Intelligence (UAI 2018)
Conference date
2018-08
Conference city
Monterey, California
Conference country
United States
Book title
Uncertainty in Artificial Intelligence (UAI) - Proceedings of the Thirty-Fourth Conference (2018)
Book author
Globerson, Amir; Silva, Ricardo
Publisher
AUAI Press
Published in
Corvallis (Oregon, USA)
Pages
329-338
Metadata
Show full item record
Author(s)
Pinot, Rafael
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Morvan, Anne
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Yger, Florian cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Gouy-Pailler, Cédric cc
Laboratoire d'Intégration des Systèmes et des Technologies [LIST (CEA)]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
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.
Subjects / Keywords
graphs