Kaiming Cheng (University of Washington), Mattea Sim (Indiana University), Tadayoshi Kohno (University of Washington), Franziska Roesner (University of Washington)

Augmented reality (AR) headsets are now commercially available, including major platforms like Microsoft’s Hololens 2, Meta’s Quest Pro, and Apple’s Vision Pro. Compared to currently widely deployed smartphone or web platforms, emerging AR headsets introduce new sensors that capture substantial and potentially privacy-invasive data about the users, including eye-tracking and hand-tracking sensors. As millions of users begin to explore AR for the very first time with the release of these headsets, it is crucial to understand the current technical landscape of these new sensing technologies and how end-users perceive and understand their associated privacy and utility implications. In this work, we investigate the current eye-tracking and hand-tracking permission models for three major platforms (HoloLens 2, Quest Pro, and Vision Pro): what is the granularity of eye-tracking and hand-tracking data made available to applications on these platforms, and what information is provided to users asked to grant these permissions (if at all)? We conducted a survey with 280 participants with no prior AR experience on Prolific to investigate (1) people’s comfort with the idea of granting eye- and hand-tracking permissions on these platforms, (2) their perceived and actual comprehension of the privacy and utility implications of granting these permissions, and (3) the self-reported factors that impact their willingness to try eye-tracking and hand-tracking enabled AR technologies in the future. Based on (mis)alignments we identify between comfort, perceived and actual comprehension, and decision factors, we discuss how future AR platforms can better communicate existing privacy protections, improve privacy-preserving designs, or better communicate risks.

View More Papers

Towards Real-time Voice Interaction Data Collection Monitoring and Ambient...

Tu Le (University of California, Irvine), Zixin Wang (Zhejiang University), Danny Yuxing Huang (New York University), Yaxing Yao (Virginia Tech), Yuan Tian (University of California, Los Angeles)

Read More

Analysis of Misconfigured IoT MQTT Deployments and a Lightweight...

Seyed Ali Ghazi Asgar, Narasimha Reddy (Texas A&M University)

Read More

Defending Against Membership Inference Attacks on Iteratively Pruned Deep...

Jing Shang (Beijing Jiaotong University), Jian Wang (Beijing Jiaotong University), Kailun Wang (Beijing Jiaotong University), Jiqiang Liu (Beijing Jiaotong University), Nan Jiang (Beijing University of Technology), Md Armanuzzaman (Northeastern University), Ziming Zhao (Northeastern University)

Read More

SCAMMAGNIFIER: Piercing the Veil of Fraudulent Shopping Website Campaigns

Marzieh Bitaab (Arizona State University), Alireza Karimi (Arizona State University), Zhuoer Lyu (Arizona State University), Adam Oest (Amazon), Dhruv Kuchhal (Amazon), Muhammad Saad (X Corp.), Gail-Joon Ahn (Arizona State University), Ruoyu Wang (Arizona State University), Tiffany Bao (Arizona State University), Yan Shoshitaishvili (Arizona State University), Adam Doupé (Arizona State University)

Read More