Out-of-Device Privacy Unveiled: Designing and Validating the Out-of-Device Privacy Scale (ODPS)

要旨

This paper proposes an Out-of-Device Privacy Scale (ODPS) - a reliable, validated psychometric privacy scale that measures users’ importance of out-of-device privacy. In contrast to existing scales, ODPS is designed to capture the importance individuals attribute to protecting personal information from out-of-device threats in the physical world, which is essential when designing privacy protection mechanisms. We iteratively developed and refined ODPS in three high-level steps: item development, scale development, and scale validation, with a total of N=1378 participants. Our methodology included ensuring content validity by following various approaches to generate items. We collected insights from experts and target audiences to understand response variability. Next, we explored the underlying factor structure using multiple methods and performed dimensionality, reliability, and validity tests to finalise the scale. We discuss how ODPS can support future work predicting user behaviours and designing protection methods to mitigate privacy risks.

著者
Habiba Farzand
University of Glasgow, Glasgow, United Kingdom
Karola Marky
Ruhr-University Bochum, Bochum, Germany
Mohamed Khamis
University of Glasgow, Glasgow, United Kingdom
論文URL

doi.org/10.1145/3613904.3642623

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Privacy in Real Contexts

313A
4 件の発表
2024-05-16 18:00:00
2024-05-16 19:20:00