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The Privacy Paradox in Smartphone Users

Published: 29 December 2022 Publication History

Abstract

In order to better understand the seemingly paradoxical online behavior of smartphone users, i.e. the privacy paradox, we conduct a survey study comprising 488 German university students. Our results indicate that users can be categorized into four different clusters according to their perception of benefits and risks associated with smartphone usage, i.e. ‘indifferent’, ‘benefit-oriented’, ‘ambivalent’ and ‘risk-oriented’ user groups. These clusters show statistically significant differences in terms of age, gender and smartphone usage as well as various privacy-related variables including privacy concerns, awareness for privacy threats, willingness to disclose personal information, trust towards providers of online services, and privacy protection behavior. Moreover, we identify factors that determine an individual’s propensity to employ privacy-protective responses. Our model can be used to predict users’ inclination to protect their online privacy based on the used operating system, their willingness to disclose personal information, reported privacy concerns, privacy literacy, and trust towards online companies.

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Cited By

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  • (2024)The Impact of Data Privacy on Users' Smartphone App Adoption DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/36765258:MHCI(1-23)Online publication date: 24-Sep-2024
  • (2024)On-device query intent prediction with lightweight LLMs to support ubiquitous conversationsScientific Reports10.1038/s41598-024-63380-614:1Online publication date: 3-Jun-2024

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cover image ACM Other conferences
MUM '22: Proceedings of the 21st International Conference on Mobile and Ubiquitous Multimedia
November 2022
315 pages
ISBN:9781450398206
DOI:10.1145/3568444
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Published: 29 December 2022

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  1. information disclosure
  2. mobile users
  3. privacy behavior prediction
  4. privacy paradox

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View all
  • (2024)The Impact of Data Privacy on Users' Smartphone App Adoption DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/36765258:MHCI(1-23)Online publication date: 24-Sep-2024
  • (2024)On-device query intent prediction with lightweight LLMs to support ubiquitous conversationsScientific Reports10.1038/s41598-024-63380-614:1Online publication date: 3-Jun-2024

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