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Privacy, Utility, Effort, Transparency and Fairness: Identifying and Swaying Trade-offs in Privacy Preserving Machine Learning through Hybrid Methods

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dc.contributor.authorEleks, Marian
dc.contributor.authorIhler Jakob
dc.contributor.authorRebstadt, Jonas
dc.contributor.authorKortum-Landwehr, Henrik
dc.contributor.authorThomas, Oliver
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:23Z
dc.date.available2024-10-21T18:24:23Z
dc.date.issued2024
dc.description.abstractAs Artificial Intelligence (AI) permeates most economic sectors, the discipline Privacy Preserving Machine Learning (PPML) gains increasing importance as a way to ensure appropriate handling of sensitive data in the machine learning process. Although PPML-methods stand to provide privacy protection in AI use cases, each one comes with a trade-off. Practitioners applying PPML-methods increasingly request an overview of the types and impacts of these trade-offs. To aid this gap in knowledge, this article applies design science research to collect trade-off dimensions and method impacts in an extensive literature review. It then evaluates the specific trade-offs with a focus group of experts and finally constructs an overview over PPML-methods and method combinations’ impact. The final trade-off dimensions are privacy, utility, effort, transparency, and fairness. Seven PPML-methods and their combinations are evaluated according to their impact in these dimensions, resulting in a vast collection of design knowledge and identified research gaps.en
dc.identifier.doi10.18420/inf2024_02
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45177
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectPrivacy Preserving Machine Learning
dc.subjectTrade-off
dc.subjectHybrid Methods
dc.subjectDesign Science
dc.titlePrivacy, Utility, Effort, Transparency and Fairness: Identifying and Swaying Trade-offs in Privacy Preserving Machine Learning through Hybrid Methodsen
dc.typeText/Conference Paper
gi.citation.endPage57
gi.citation.publisherPlaceBonn
gi.citation.startPage43
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitle5. Privacy & Security at Large Workshop

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