Poster: Unobtrusively Mining Vital Sign and Embedded Sensitive Info via AR/VR Motion Sensors
Document Type
Conference Proceeding
Publication Date
10-23-2023
Abstract
Despite the rapid growth of augmented reality and virtual reality (AR/VR) in various applications, the understanding of information leakage through sensor-rich headsets remains in its infancy. In this poster, we investigate an unobtrusive privacy attack, which exposes users' vital signs and embedded sensitive information (e.g., gender, identity, body fat ratio), based on unrestricted AR/VR motion sensors. The key insight is that the headset is closely mounted on the user's face, allowing the motion sensors to detect facial vibrations produced by users' breathing and heartbeats. Specifically, we employ deep-learning techniques to reconstruct vital signs, achieving signal qualities comparable to dedicated medical instruments, as well as deriving users' gender, identity, and body fat information. Experiments on three types of commodity AR/VR headsets reveal that our attack can successfully reconstruct high-quality vital signs, detect gender (accuracy over 93.33%), re-identify users (accuracy over 97.83%), and derive body fat ratio (error less than 4.43%).
Identifier
85176129251 (Scopus)
ISBN
[9781450399265]
Publication Title
Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing Mobihoc
External Full Text Location
https://doi.org/10.1145/3565287.3623624
First Page
308
Last Page
309
Grant
CCF2000480
Fund Ref
National Science Foundation
Recommended Citation
Zhang, Tianfang; Ye, Zhengkun; Mahdad, Ahmed Tanvir; Akanda, Md Mojibur Rahman Redoy; Shi, Cong; Saxena, Nitesh; Wang, Yan; and Chen, Yingying, "Poster: Unobtrusively Mining Vital Sign and Embedded Sensitive Info via AR/VR Motion Sensors" (2023). Faculty Publications. 1373.
https://digitalcommons.njit.edu/fac_pubs/1373
