"My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security

By Ruogu Kang, Laura A. Dabbish, Nathaniel Fruchter, Sara B. Kiesler. 2015

354 citations recorded by the source corpus.

Subfields: Privacy & Security, AI Ethics, Fairness & Society

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Citations in the corpus

MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound (2022, methodology): “When YouTube users upload videos, they seem to understand details of its privacy policy, beyond consenting to it [64].”
MERLOT: Multimodal Neural Script Knowledge Models (2021, background): “Possibly due to cues of engagement like view/subscriber counts, users on YouTube appear to understand the privacy implications of uploading a ‘public’ video [55], differentiating YouTube from more private venues, like email and social media.”
User Perceptions of Smart Home IoT Privacy (2018, background): “These results support previous work on user behaviors in contexts other than smart home IoT [ 1, 14, 25, 53, 58], and indicate that convenience remains a primary justiﬁcation for sacriﬁcing privacy for IoT device owners.”
Imagined Connectivities: Synthesized Conceptions of Public Wi-Fi in Urban India (2017, background): “on Internet mental models [39] and Klasnja et al.”
End User Security and Privacy Concerns with Smart Homes (2017, methodology): “, email encryption [49], internet privacy [34]).”
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context (2020, background): “uter Interaction (HCI) and Science and Technology studies (STS) scholars have long highlighted the struggle of technologists to identify and incorporate these factors into their development processes [14, 49, 57, 58, 64]. More recently, ML fairness scholars have argued the current ML system design p”
Secrets and Likes: The Drive for Privacy and the Difficulty of Achieving It in the Digital Age (2020, background): “In an in-depth interview study, Kang et al. (2015) found that 77% of non-technical participants reported taking some action to hide or delete their digital traces, including using anonymous search engines and blockers for cookies and other trackers.”