VisGuardian: A Lightweight Group-based Visual Privacy Control Technique For Smart Glasses in Home Environments

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Authors: Shuning Zhang, Qucheng Zang, Yongquan 'Owen' Hu, Jiachen Du, Xueyang Wang, Yankun Kong, Xinyi Fu, Suranga Chandima Nanayakkara, Xin Qiang Yi, Hewu Li
Cited by: 1
References: 115

Abstract

Always-on sensing of AI applications on AR glasses makes traditional permission techniques inefficient for context-dependent private visual data within home environments. Home presents a challenging privacy context due to massive sensitive objects and the intimate nature of daily routines. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.

Fields of Study & Subjects

Advanced Malware Detection Techniques User Authentication and Security Systems Adversarial Robustness in Machine Learning

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–22
Publication date
13 April 2026