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Privacy-Preserving Stress Detection Using Smartwatch Health Data

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dc.contributor.authorLange, Lucas
dc.contributor.authorDegenkolb, Borislav
dc.contributor.authorRahm, Erhard
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:32Z
dc.date.available2023-11-29T14:50:32Z
dc.date.issued2023
dc.description.abstractWe present the first privacy-preserving approach for stress detection from wrist-worn wearables based on the Time-Series Classification Transformer (TSCT) architecture and incorporating Differential Privacy (DP) to ensure provable privacy guarantees. The non-private baseline results prove the TSCT to be an effective model for the given task. Our DP experiments then show that the private models suffer from reduced utility but can still be used for reliable stress detection depending on the application. Our proposed approach has potential applications in smart health, where it can be used to monitor smartwatch users’ stress levels without compromising their privacy and provide timely interventions or suggestions to prevent adverse health outcomes. Another primary contribution is our evaluation, which studies and shows negative effects of DP regarding model training. The results of this work provide perspectives for future research and applications whenever the fields of stress detection and data privacy intervene.en
dc.identifier.doi10.18420/inf2023_66
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43188
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectStress Detection
dc.subjectStress Recognition
dc.subjectSmartwatch
dc.subjectTime-Series Classification Transformer
dc.subjectDifferential Privacy
dc.subjectPrivacy-Preserving Machine Learning
dc.titlePrivacy-Preserving Stress Detection Using Smartwatch Health Dataen
dc.typeText/Conference Paper
gi.citation.endPage560
gi.citation.publisherPlaceBonn
gi.citation.startPage549
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleCybersecurity & Privatsphäre - 4. Interdisciplinary Privacy Security at Large Workshop

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten