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Privacy dark patterns in identity management
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (PriSec)
2017 (English)In: Open Identity Summit 2017: Proceedings / [ed] Lothar Fritsch, Heiko Roßnagel, Detlef Hühnlein, Bonn: Gesellschaft für Informatik, 2017, p. 93-104Conference paper, Published paper (Refereed)
Abstract [en]

This article presents three privacy dark patterns observed in identity management. Dark patterns are software design patterns that intentionally violate requirements, in the given case privacy requirements for identity management. First, the theoretical background is presented, and then next, the observed patterns are documented, described and formalized. The resulting dark patterns show how security is used as obfuscation of data collection, how the seemingly harmless collection of additional data is advertised to end users, and how the use of anonymization technology is actively discouraged by service providers.

Place, publisher, year, edition, pages
Bonn: Gesellschaft für Informatik, 2017. p. 93-104
Series
Lecture Notes in Informatics, ISSN 1617-5468 ; 277
Keywords [en]
privacy patterns, dark patterns, identity management, identity attribute extraction, dark patterns, GDPR
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-63722ISBN: 978-3-88579-671-8 (print)OAI: oai:DiVA.org:kau-63722DiVA, id: diva2:1141673
Conference
Open Identity Summit (OID), 5-6 october 2017, Karlstad, Sweden.
Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2018-04-24Bibliographically approved

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fulltext(609 kB)20 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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