Al Muhander, Bayan, Arachchilage, Nalin, Majib, Yasar, Alosaimi, Mohammed, Rana, Omar F. ![]() ![]() |
Abstract
People are increasingly bringing Internet of Things (IoT) devices into their homes without understanding how their data is gathered, processed, and used. We describe PrivacyCube, a novel data physicalization designed to increase privacy awareness within smart home environments. PrivacyCube visualizes IoT data consumption by displaying privacy-related notices. PrivacyCube aims to assist smart home occupants to (i) understand their data privacy better and (ii) have conversations around data management practices of IoT devices used within their homes. Using PrivacyCube, households can learn and make informed privacy decisions collectively. To evaluate PrivacyCube, we used multiple research methods throughout the different stages of design. We first conducted a focus group study in two stages with six participants to compare PrivacyCube to text and state-of-the-art privacy policies. We then deployed PrivacyCube in a 14-day-long in-home field study with eight households. Lastly, we conducted an event-based field study comparing PrivacyCube with a mobile application, engaging 26 participants with diverse demographics. Our results show that PrivacyCube helps home occupants comprehend IoT privacy better with significantly increased privacy awareness at p <.05 (p=0.00041, t= -5.57). Participants preferred PrivacyCube over text privacy policies because it was comprehensive and easier to use. PrivacyCube, Privacy Label, and the mobile application, all received positive reviews from participants, with PrivacyCube being preferred for its interactivity and ability to encourage conversations. PrivacyCube was also considered by home occupants as a piece of home furniture , encouraging them to socialize and discuss IoT privacy implications using this device. Watch the demo (Demo Video) (Source Code).
Item Type: | Article |
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Date Type: | Published Online |
Status: | In Press |
Schools: | Schools > Computer Science & Informatics |
Publisher: | Association for Computing Machinery (ACM) |
ISSN: | 2691-1914 |
Date of Acceptance: | 20 February 2025 |
Last Modified: | 19 Mar 2025 11:00 |
URI: | https://orca.cardiff.ac.uk/id/eprint/176991 |
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