Eliciting Security & Privacy-Informed Sharing Techniques for Multi-User Augmented Reality

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Abstract

The HCI community has explored new interaction designs for collaborative AR interfaces in terms of usability and feasibility; however, security & privacy (S&P) are often not considered in the design process and left to S&P professionals. To produce interaction proposals with S&P in mind, we extend the user-driven elicitation method with a scenario-based approach that incorporates a threat model involving access control in multi-user AR. We conducted an elicitation study in two conditions, pairing AR/AR experts in one condition and AR/S&P experts in the other, to investigate the impact of each pairing. We contribute a set of expert-elicited interactions for sharing AR content enhanced with access control provisions, analyze the benefits and tradeoffs of pairing AR and S&P experts, and present recommendations for designing future multi-user AR interactions that better balance competing design goals of usability, feasibility, and S&P in collaborative AR.

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Rajaram, S., Chen, C., Roesner, F., & Nebeling, M. (2023). Eliciting Security & Privacy-Informed Sharing Techniques for Multi-User Augmented Reality. In Conference on Human Factors in Computing Systems - Proceedings. Association for Computing Machinery. https://doi.org/10.1145/3544548.3581089

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