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Exploring User-Suitable Metaphors for Differentially Private Data Analyses
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-2823-3837
Karlstad University, Faculty of Arts and Social Sciences (starting 2013), Karlstad Business School (from 2013).ORCID iD: 0000-0002-6509-3792
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Chalmers University of Technology.ORCID iD: 0000-0002-6938-4466
2022 (English)In: Proceedings of the 18th Symposium on Usable Privacy and Security, SOUPS 2022, 2022, p. 175-193Conference paper, Published paper (Refereed)
Abstract [en]

Despite recent enhancements in the deployment of differential privacy (DP), little has been done to address the human aspects of DP-enabled systems. Comprehending the complex concept of DP and the privacy protection it provides could be challenging for lay users who should make informed decisions when sharing their data. Using metaphors could be suitable to convey key protection functionalities of DP to them. Based on a three-phase framework, we extracted and generated metaphors for differentially private data analysis models (local and central). We analytically evaluated the metaphors based on experts’ feedback and then empirically evaluated them in online interviews with 30 participants. Our results showed that the metaphorical explanations can successfully convey that perturbation protects privacy and that there is a privacy-accuracy trade-off. Nonetheless, conveying information at a high level leads to incorrect expectations that negatively affect users’ understanding and limits the ability to apply the concept to different contexts. In this paper, we presented the plausible suitability of metaphors and discussed the challenges of using them to facilitate informed decisions on sharing data with DP-enabled systems. 

Place, publisher, year, edition, pages
2022. p. 175-193
Keywords [en]
Data analysis models; Differential privacies; Expert feedback; Human aspects; Informed decision; Key protections; Privacy protection; Private data analysis; Three phase; Three phasis, Conveying
National Category
Computer Sciences
Research subject
Computer Science; Information Systems
Identifiers
URN: urn:nbn:se:kau:diva-92509Scopus ID: 2-s2.0-85140926705ISBN: 9781939133304 (print)OAI: oai:DiVA.org:kau-92509DiVA, id: diva2:1711341
Conference
18th Symposium on Usable Privacy and Security (SOUPS), Boston, United States, August 7–9, 2022.
Available from: 2022-11-16 Created: 2022-11-16 Last updated: 2022-11-24Bibliographically approved

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Karegar, FarzanehAlaqra, Ala SarahFischer-Hübner, Simone

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Karegar, FarzanehAlaqra, Ala SarahFischer-Hübner, Simone
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Citation style
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