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Graph-based Clustering under Differential Privacy

hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorPinot, Rafael
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorMorvan, Anne
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
hal.structure.identifierLaboratoire d'Intégration des Systèmes et des Technologies [LIST (CEA)]
dc.contributor.authorGouy-Pailler, Cédric
HAL ID: 6827
ORCID: 0000-0003-1298-7845
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorAtif, Jamal
HAL ID: 15689
dc.date.accessioned2019-07-02T10:39:49Z
dc.date.available2019-07-02T10:39:49Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19106
dc.language.isoenen
dc.subjectgraphsen
dc.subject.ddc006.3en
dc.titleGraph-based Clustering under Differential Privacyen
dc.typeCommunication / Conférence
dc.description.abstractenIn 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.en
dc.identifier.citationpages329-338en
dc.relation.ispartoftitleUncertainty in Artificial Intelligence (UAI) - Proceedings of the Thirty-Fourth Conference (2018)en
dc.relation.ispartofeditorGloberson, Amir
dc.relation.ispartofeditorSilva, Ricardo
dc.relation.ispartofpublnameAUAI Pressen
dc.relation.ispartofpublcityCorvallis (Oregon, USA)en
dc.relation.ispartofdate2018
dc.contributor.countryeditoruniversityotherFRANCE
dc.subject.ddclabelIntelligence artificielleen
dc.relation.conftitleConference on Uncertainty in Artificial Intelligence (UAI 2018)en
dc.relation.confdate2018-08
dc.relation.confcityMonterey, Californiaen
dc.relation.confcountryUnited Statesen
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-03-29T17:52:44Z
hal.identifierhal-02170699*
hal.version1*
hal.update.actionupdateFiles*
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