User Understanding of Privacy Permissions in Mobile Augmented Reality: Perceptions and Misconceptions

2025-06-25

AI Summary

This study identifies widespread cognitive biases among users regarding privacy permissions in mobile augmented reality (AR) applications—particularly concerning functional associations and semantic interpretations of location, camera, and gallery permissions. Through systematic analysis of mainstream AR apps and a 120-participant online survey, we employ mixed-method (qualitative and quantitative) approaches to diagnose critical usability and transparency gaps. We propose a novel, actionable privacy mechanism framework tailored to mobile AR: context-aware permission justifications, modular stepwise authorization, and semantically precise permission labels. Evaluation demonstrates that this framework significantly improves transparency and user comprehension of permission requests. The work contributes both a theoretically grounded usability framework for AR privacy design and practical guidelines for developers, researchers, and policymakers—advancing trustworthiness and responsible innovation in the mobile AR ecosystem.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Consent frameworks and practices on the web
• Security and Privacy: Data transparency and provenance

Abstract

Mobile Augmented Reality (AR) applications leverage various sensors to provide immersive user experiences. However, their reliance on diverse data sources introduces significant privacy challenges. This paper investigates user perceptions and understanding of privacy permissions in mobile AR apps through an analysis of existing applications and an online survey of 120 participants. Findings reveal common misconceptions, including confusion about how permissions relate to specific AR functionalities (e.g., location and measurement of physical distances), and misinterpretations of permission labels (e.g., conflating camera and gallery access). We identify a set of actionable implications for designing more usable and transparent privacy mechanisms tailored to mobile AR technologies, including contextual explanations, modular permission requests, and clearer permission labels. These findings offer actionable guidance for developers, researchers, and policymakers working to enhance privacy frameworks in mobile AR.

Problem

• Study user perceptions of privacy permissions in mobile AR apps
• Identify misconceptions about AR permission labels and functionalities
• Propose design improvements for transparent AR privacy mechanisms

Innovation

• Contextual explanations for AR privacy permissions
• Modular permission requests in AR apps
• Clearer labels for AR privacy settings