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Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces

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dc.contributor.authorSörries, Peter
dc.contributor.authorMüller-Birn, Claudia
dc.contributor.authorGlinka, Katrin
dc.contributor.authorBoenisch, Franziska
dc.contributor.authorMargraf, Marian
dc.contributor.authorSayegh-Jodehl, Sabine
dc.contributor.authorRose, Matthias
dc.contributor.editorWienrich, Carolin
dc.contributor.editorWintersberger, Philipp
dc.contributor.editorWeyers, Benjamin
dc.date.accessioned2021-09-23T10:52:32Z
dc.date.available2021-09-23T10:52:32Z
dc.date.issued2021
dc.description.abstractThe application of machine learning (ML) in the medical domain has recently received a lot of attention. However, the constantly growing need for data in such ML-based approaches raises many privacy concerns, particularly when data originate from vulnerable groups, for example, people with a rare disease. In this context, a challenging but promising approach is the design of privacy-preserving computation technologies (e.g. differential privacy). However, design guidance on how to implement such approaches in practice has been lacking. In our research, we explore these challenges in the design process by involving stakeholders from medicine, security, ML, and human-computer interaction, as well as patients themselves. We emphasize the suitability of reflective design in this context by considering the concept of privacy by design. Based on a real-world use case situated in the healthcare domain, we explore the existing privacy needs of our main stakeholders, i.e. medical researchers or physicians and patients. Stakeholder needs are illustrated within two scenarios that help us to reflect on contradictory privacy needs. This reflection process informs conceptional design rationales and our proposal for privacy-preserving explanation user interfaces. We propose that the latter support both patients’ privacy preferences for a meaningful data donation and experts’ understanding of the privacy-preserving computation technology employed.en
dc.identifier.doi10.18420/muc2021-mci-wsc-389
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37418
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2021 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectPrivacy preservation
dc.subjectmachine learning
dc.subjectuser interface
dc.subjectreflective design
dc.subjectconceptional design rationales
dc.titlePrivacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfacesen
dc.typeText/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date5.-8. September 2021
gi.conference.locationIngolstadt
gi.conference.sessiontitleMCI-WS14: Usable Security und Privacy Workshop
gi.document.qualitydigidoc

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