Applying Differential Privacy to Machine Learning: Challenges and Potentials
| dc.contributor.author | Boenisch, Franziska | |
| dc.contributor.editor | Selhorst, Marcel | |
| dc.contributor.editor | Loebenberger, Daniel | |
| dc.contributor.editor | Nüsken, Michael | |
| dc.date.accessioned | 2019-12-02T13:06:03Z | |
| dc.date.available | 2019-12-02T13:06:03Z | |
| dc.date.issued | 2019 | |
| dc.identifier.doi | 10.18420/cdm-2019-31-26 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/30621 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. / FG KRYPTO | |
| dc.relation.ispartof | crypto day matters 31 | |
| dc.relation.ispartofseries | crypto day matters | |
| dc.title | Applying Differential Privacy to Machine Learning: Challenges and Potentials | en |
| dc.type | Text/Abstract | |
| gi.citation.publisherPlace | Bonn | |
| gi.conference.date | 17.-18.10.2019 | |
| gi.conference.location | Qualcomm, Berlin | |
| gi.conference.sessiontitle | Kurzbeitrag |
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