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Augmented reality (AR) technologies, such as Microsoft’s HoloLens head-mounted display and AR-enabled car windshields, are rapidly emerging. AR applications provide users with immersive virtual experiences by capturing input from a user’s surroundings and overlaying virtual output on the user’s perception of the real world. These applications enable users to interact with and perceive virtual content in fundamentally new ways. However, the immersive nature of AR applications raises serious security and privacy concerns. Prior work has focused primarily on input privacy risks stemming from applications with unrestricted access to sensor data. However, the risks associated with malicious or buggy AR output remain largely unexplored. For example, an AR windshield application could intentionally or accidentally obscure oncoming vehicles or safety-critical output of other AR applications. In this work, we address the fundamental challenge of securing AR output in the face of malicious or buggy applications. We design, prototype, and evaluate Arya, an AR platform that controls application output according to policies specified in a constrained yet expressive policy framework. In doing so, we identify and overcome numerous challenges in securing AR output.
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This content will become publicly available on May 23, 2026
Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
- Award ID(s):
- 2238424
- PAR ID:
- 10621716
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Automatic Control
- ISSN:
- 0018-9286
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
