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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. (PriSec)
2017 (English)In: Open Identity Summit 2017: Proceedings / [ed] Lothar Fritsch, Heiko Roßnagel, Detlef Hühnlein, 2017, 93-104 p.Conference 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
2017. 93-104 p.
Series
Lecture Notes in Informatics, ISSN 1617-5468 ; 277
Keyword [en]
privacy patterns, dark patterns, identity management, identity attribute extraction, dark patterns, GDPR
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-63722ISBN: 978-3-88579-671-8 (print)OAI: oai:DiVA.org:kau-63722DiVA: diva2:1141673
Conference
Open Identity Summit (OID) 2017
Available from: 2017-09-15 Created: 2017-09-15 Last updated: 2017-09-15

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