Privacy Equilibrium: Balancing Privacy Needs in Dynamic Multi-User Augmented Reality Scenarios

DOI

As augmented reality (AR) glasses become more widely used in public settings, a key challenge is meeting the privacy needs of multiple AR users and bystanders in a fine-grained manner. To enable this, we present a conceptual framework for Privacy Equilibrium--balancing user experience (UX) and privacy between all individuals in a shared space. The framework applies constrained optimization to compute AR sensing policies that grant or restrict permissions to maximize UX while minimizing privacy risks (e.g., capturing bystanders or sensitive environmental data). We instantiate this framework in a simulation and analysis toolkit to holistically evaluate optimization strategies and visualize tradeoffs between UX and privacy. Through application scenarios, we demonstrate the flexibility of our optimization approach to minimize these tradeoffs across conflicting user needs and privacy preferences. Walkthrough evaluations with AR and security & privacy researchers highlight the potential of our framework and toolkit to inform future privacy-mediating techniques for AR.