Enabling Safer Augmented Reality Experiences: Usable Privacy Interventions for AR Creators and End-Users

Augmented reality (AR) is approaching everyday usage, but poses novel privacy concerns for end-users and bystanders due to how AR devices capture users and process physical environments. To enable the benefits of AR while balancing privacy goals, my dissertation develops tools and frameworks to guide AR creators and users to address privacy risks that can arise with AR. First, I explore how to enable AR designers to interactively analyze potential risks in their prototypes through implicit threat modeling within AR authoring tools. Next, through elicitation studies with AR and privacy experts, I contribute frameworks to expand AR interaction models with privacy-friendlier alternatives to traditional AR input, output, and interaction techniques. Lastly, I develop a suite of AI-enabled Privacy Assistant techniques to raise users’ awareness of privacy risks and help them adapt AR interfaces accordingly. Ultimately, my dissertation promotes an AR ecosystem with privacy at the forefront by equipping AR creators and users with a privacy mindset.