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In this study, we examine the ways in which user attitudes towards privacy and security relating to mobile devices and the data stored thereon may impact the strength of unlock authentication, focusing on Androids graphical unlock patterns. We conducted an online study with Amazon Mechanical Turk ($N=750$) using self-reported unlock authentication choices, as well as Likert scale agreement/disagreement responses to a set of seven privacy/security prompts. We then analyzed the responses in multiple dimensions, including a straight average of the Likert responses as well as using Principle Component Analysis to expose latent factors. We found that responses to two of the seven questions proved relevant and significant. These two questions considered attitudes towards general concern for data stored on mobile devices, and attitudes towards concerns for unauthorized access by known actors. Unfortunately, larger conclusions cannot be drawn on the efficacy of the broader set of questions for exposing connections between unlock authentication strength (Pearson Rank $r=-0.08$, $p<0.1$). However, both of our factor solutions exposed differences in responses for demographics groups, including age, gender, and residence type. The findings of this study suggests that there is likely a link between perceptions of privacy/security on mobile devices and the perceived threats therein, but more research is needed, particularly on developing better survey and measurement techniques of privacy/security attitudes that relate to mobile devices specifically.
Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, there is only
Gamification and Serious Games are progressively being used over a host of fields, particularly to support education. Such games provide a new way to engage students with content and can complement more traditional approaches to learning. This articl
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Differential privacy protects an individuals privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate diff
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of IoT applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of