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Learning without Looking: Similarity Preserving Hashing and Its Potential for Machine Learning in Privacy Critical Domains

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dc.contributor.authorEleks,Marian
dc.contributor.authorRebstadt,Jonas
dc.contributor.authorFukas,Philipp
dc.contributor.authorThomas,Oliver
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:16Z
dc.date.available2022-09-28T17:10:16Z
dc.date.issued2022
dc.description.abstractMachine Learning is frequently ranked as one of the most promising technologies in several application domains but falls short when the data necessary for training is privacy-sensitive and can thus not be used. We address this problem by extending the field of Privacy Aware Machine Learning with the application of Similarity Preserving Hashing algorithms to the task of data anonymization in a Design Science Research approach. In this endeavor, novel anonymization algorithms made to enable Machine Learning on anonymized data are designed, implemented, and evaluated. Throughout the Design Science Research process, we present a collection of issues and requirements for Privacy Aware Machine Learning algorithms along with three Similarity Preserving Hashing-based algorithms to fulfil them. A metric-based comparison of established and novel algorithms as well as new arising opportunities for Machine Learning on sensitive data are also added to the current knowledge base of Information Systems research.en
dc.identifier.doi10.18420/inf2022_16
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39513
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectData Anonymization
dc.subjectSimilarity Preserving Hashing
dc.subjectMachine Learning
dc.subjectFuzzy Hashing
dc.subjectPrivacy Aware Machine Learning
dc.titleLearning without Looking: Similarity Preserving Hashing and Its Potential for Machine Learning in Privacy Critical Domainsen
gi.citation.endPage177
gi.citation.startPage161
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitle3. Interdisciplinary Privacy & Security at Large Workshop (Privacy&Security@Large)

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften