Date of Award

August 2021

Document Type


Degree Name

Doctor of Philosophy (PhD)


Human Centered Computing

Committee Member

Bart Knijnenburg

Committee Member

Kelly Caine

Committee Member

Alexander Herzog

Committee Member

Pamela Wisniewski


Smart Home IoT is gaining popularity because of its ability to render a connected experience and a high level of automation to its users. To render this connected experience, smart home devices need to collect and share data from their environments. From privacy and security standpoint, the data collection can be an important cause of concern for smart home IoT users. The research presented in this dissertation is focused on understanding how smart home IoT users make privacy decisions. With the understanding of these decision preferences, privacy settings interfaces for smart home users are created which can be helpful in setting privacy preferences effectively.

In this dissertation, privacy decision making in smart home IoT is investigated from three angles: First, understanding how contextual factors such as entities collecting/receiving data and storage of location influences privacy decisions. Second, investigating how factors like heuristics (in form of defaults and framing) and personality characteristics which lie outside of decision making context influence privacy decisions. Third, how do conceptual models associated with smart homes influence the privacy management experience of smart home IoT users.

In a controlled experiment which presented participants with multiple contextual scenarios, the data analysis showed that the participants tend to emphasize some contextual factors more over the others and that their decision making is influenced by heuristics like defaults and framing. The regression modeling results of privacy decisions informed the design of privacy settings interfaces which can be used to manage privacy decision in smart homes. By using machine learning methods, participants were clustered on the basis of similarity in their privacy decisions. Upon further analysis of these clusters, it was observed that the interface design needs of participants varied across different clusters.

This observation led to the creation of personalized privacy management interfaces. In another controlled experiment, these personalized interfaces were tested with participants and the findings revealed that the personalization of interfaces rendered better experience to the users. This controlled experiment also accounted for smart home users conceptual models by leveraging psychometric scales which gauged whether a person draws from two distinct conceptual models (`Agentic' and `User Centric') of adoption. The affinity of IoT users towards either of these conceptual models was gauged using newly developed psychometric scales which were built during a preceding survey study. The results from this study showed the robustness of the new scales across different cultural groups as well as their effectiveness in influencing user perceptions of different adoption and experience related aspects of smart home technology.



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