Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms

• URL: http://arxiv.org/abs/2308.06148v1
• Date: Fri, 11 Aug 2023 14:25:01 GMT
• Title: Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms
• Authors: Piero Romare, Victor Morel, Farzaneh Karegar, Simone Fischer-H\"ubner
• Abstract summary: The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT) TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT). TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions. As personal data flows between different entities, there is a potential for privacy concerns. In this study, we aimed to identify the privacy factors that impact users' concerns and preferences for using IoT TAPs. To address this research objective, we conducted three focus groups with 15 participants and we extracted nine themes related to privacy factors using thematic analysis. Our participants particularly prefer to have control and transparency over the automation and are concerned about unexpected data inferences, risks and unforeseen consequences for themselves and for bystanders that are caused by the automation. The identified privacy factors can help researchers derive predefined and selectable profiles of privacy permission settings for IoT TAPs that represent the privacy preferences of different types of users as a basis for designing usable privacy controls for IoT TAPs.

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