MINERVA: Secure Collaborative Machine Tool Data Utilization Leveraging Confidentiality-Protecting Technologies
| dc.contributor.author | Ludwig, Andy | |
| dc.contributor.author | Heinl, Michael P. | |
| dc.contributor.author | Giehl, Alexander | |
| dc.contributor.editor | Roßnagel, Heiko | |
| dc.contributor.editor | Schunck, Christian H. | |
| dc.contributor.editor | Sousa, Filipe | |
| dc.date.accessioned | 2024-06-07T08:59:57Z | |
| dc.date.available | 2024-06-07T08:59:57Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The digitization of shop floors opens up opportunities for innovative applications and business models due to the vast amount of generated data. However, a lot of this potential is currently not utilized because companies consider the risk of data sharing as too high compared to the corresponding benefit. Focusing on the machine tool sector, the research project MINERVA addresses these concerns by experimentally repurposing privacy-enhancing technologies as confidentialityprotecting technologies and applying them to the use case of condition monitoring to protect intellectual property and other information deemed critical by machine tool operators. Thereby, MINERVA’s goal is to reduce the risk of data sharing and support the establishment of data-driven business models in the machine tool sector in the long term. | en |
| dc.identifier.doi | 10.18420/OID2024_15 | |
| dc.identifier.isbn | 978-3-88579-744-9 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/44095 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Open Identity Summit 2024 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-350 | |
| dc.subject | Machine Tool Data | |
| dc.subject | Confidentiality-Protecting Technologies | |
| dc.subject | Privacy-Enhancing Technologies | |
| dc.subject | Industrial Internet of Things | |
| dc.subject | Defense-in-Depth | |
| dc.subject | Supply Chain Security | |
| dc.title | MINERVA: Secure Collaborative Machine Tool Data Utilization Leveraging Confidentiality-Protecting Technologies | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 176 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 171 | |
| gi.conference.date | 20.-21. June 2024 | |
| gi.conference.location | Porto, Portugal | |
| gi.conference.sessiontitle | Short Paper |
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