Privacy Aware Processing
| dc.contributor.author | Eleks, Marian | |
| dc.contributor.author | Rebstadt, Jonas | |
| dc.contributor.author | Kortum, Henrik | |
| dc.contributor.author | Thomas, Oliver | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.date.accessioned | 2023-11-29T14:50:32Z | |
| dc.date.available | 2023-11-29T14:50:32Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In many machine learning (ML) applications, the provision of data and the training as well as the analysis of machine learning systems are performed by distinct actors, a data owner and a data consumer. To protect sensitive information in these ML-scenarios, privacy aware machine learning (PAML) methods are often applied to the data before sharing. Based on the type of PAML methods used, data understanding and preparation as defined in the CRISP-DM model become more difficult if not impossible. To enable these steps, we propose a method to share a variety of uncritical information with the data consumer who is then able to define the necessary processing steps on a meta-level. These are then applied to the data in the data owners local trusted environment before the PAML-methods whereupon the prepared and protected data is shared. | en |
| dc.identifier.doi | 10.18420/inf2023_67 | |
| dc.identifier.isbn | 978-3-88579-731-9 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43189 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2023 - Designing Futures: Zukünfte gestalten | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-337 | |
| dc.subject | Privacy Aware Machine Learning | |
| dc.subject | Data Understanding | |
| dc.subject | Data Preparation | |
| dc.subject | CRISP-DM | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Machine Learning | |
| dc.title | Privacy Aware Processing | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 569 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 561 | |
| gi.conference.date | 26.-29. September 2023 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Cybersecurity & Privatsphäre - 4. Interdisciplinary Privacy Security at Large Workshop |
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