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PISA: A Privacy Impact Self-assessment App Using Personas to Relate App Behavior to Risks to Smartphone Users
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-0418-4121
2020 (English)In: Communications in Computer and Information Science, CCIS, Springer, 2020, Vol. 1226, p. 613-621Conference paper, Published paper (Refereed)
Abstract [en]

We present an educative self-assessment app intended to increase awareness of app-related privacy risks. The privacy impact self-assessment (PISA) app is intended to stimulate smartphone user reflection over risks of data sharing and data extraction from their smartphones. An interactive user interface performs an end-user targeted dialogue about apps using personas with a variety of vulnerabilities. The guided dialogue about threats is intended to engage the user’s reflection about own app risk. We describe the underlying model and interaction design, summarize the personas and discuss the user interfaces implemented in the app.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 1226, p. 613-621
Series
Communications in Computer and Information Science book series, ISSN 1865-0929, E-ISSN 1865-0937
Keywords [en]
Privacy impact awareness, Privacy personas, Privacy risk, Smartphone apps, User education, User interface, Data Sharing, Human computer interaction, Risk assessment, Smartphones, Data extraction, End users, Interaction design, Interactive user interfaces, Privacy risks, Self assessment, User interfaces
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-82949DOI: 10.1007/978-3-030-50732-9_79Scopus ID: 2-s2.0-85088741692ISBN: 978-3-030-50731-2 (print)ISBN: 978-3-030-50732-9 (electronic)OAI: oai:DiVA.org:kau-82949DiVA, id: diva2:1529686
Conference
International Conference on Human-Computer Interaction, HCI International 2020, 19 July 2020 through 24 July 2020
Available from: 2021-02-19 Created: 2021-02-19 Last updated: 2021-04-28Bibliographically approved

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Fritsch, Lothar

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf