2025/11/14 by Piero Romare, Romare, Piero, Farzaneh Karegar +3
Computer Science · Social Sciences · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #IoT and Edge/Fog Computing #Mobile Crowdsensing and Crowdsourcing #Privacy, Security, and Data Protection
paper · pdf · doi:10.48550/arxiv.2511.11209
openalex publication_date 2025/11/14 · openalex created_date 2025/11/18 · openalex updated_date 2026/07/28
IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls. Even when fine-grained controls are available, users are likely overwhelmed by the complexity of setting privacy preferences. This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users that can be semi-automatically assigned or suggested to them. We developed and validated a questionnaire, based on users' privacy concerns regarding confidentiality and control and their requirements towards transparency in TAPs. In an online study (N=301), where participants were informed about potential privacy risks, we clustered users by their privacy concerns and requirements into Basic, Medium and High Privacy clusters. These clusters were then characterized by the users' data sharing preferences, based on a factorial vignette approach, considering the data categories, the data recipient types, and the purpose of data sharing. Our findings show three distinct privacy profiles, providing a foundation for more usable privacy controls in TAPs.