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dc.contributor.authorPinot, Rafaël
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
dc.contributor.authorGouy-Pailler, Cedric
HAL ID: 6827
ORCID: 0000-0003-1298-7845
dc.contributor.authorAtif, Jamal
HAL ID: 15689
dc.date.accessioned2020-10-23T09:50:09Z
dc.date.available2020-10-23T09:50:09Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21149
dc.language.isoenen
dc.subjectDifferential Privacy
dc.subjectAdversarial Examples
dc.subjectRenyi divergence
dc.subject.ddc005en
dc.titleA unified view on differential privacy and robustness to adversarial examples
dc.typeCommunication / Conférence
dc.description.abstractenThis short note highlights some links between two lines of research within the emerging topic of trustworthy machine learning: differential privacy and robustness to adversarial examples. By abstracting the definitions of both notions, we show that they build upon the same theoretical ground and hence results obtained so far in one domain can be transferred to the other. More precisely, our analysis is based on two key elements: probabilistic mappings (also called randomized algorithms in the differential privacy community), and the Renyi divergence which subsumes a large family of divergences. We first generalize the definition of robustness against adversarial examples to encompass probabilistic mappings. Then we observe that Renyi-differential privacy (a generalization of differential privacy recently proposed in [10]) and our definition of robustness share several similarities. We finally discuss how can both communities benefit from this connection to transfer technical tools from one research field to the other.
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-02892170
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.conftitleWorkshop on Machine Learning for CyberSecurity at ECMLPKDD 2019
dc.relation.confdate2019-09
dc.relation.confcityWurzburg
dc.relation.confcountryGERMANY
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2021-01-12T15:15:20Z


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