TY - GEN
T1 - Can Virtual Reality Protect Users from Keystroke Inference Attacks?
AU - Yang, Zhuolin
AU - Sarwar, Zain
AU - Hwang, Iris
AU - Bhaskar, Ronik
AU - Zhao, Ben Y.
AU - Zheng, Haitao
N1 - Publisher Copyright: © USENIX Security Symposium 2024.All rights reserved.
PY - 2024
Y1 - 2024
N2 - Virtual Reality (VR) has gained popularity by providing immersive and interactive experiences without geographical limitations. It also provides a sense of personal privacy through physical separation. In this paper, we show that despite assumptions of enhanced privacy, VR is unable to shield its users from side-channel attacks that steal private information. Ironically, this vulnerability arises from VR's greatest strength, its immersive and interactive nature. We demonstrate this by designing and implementing a new set of keystroke inference attacks in shared virtual environments, where an attacker (VR user) can recover the content typed by another VR user by observing their avatar. While the avatar displays noisy telemetry of the user's hand motion, an intelligent attacker can use that data to recognize typed keys and reconstruct typed content, without knowing the keyboard layout or gathering labeled data. We evaluate the proposed attacks using IRB-approved user studies across multiple VR scenarios. For 13 out of 15 tested users, our attacks accurately recognize 86%-98% of typed keys, and the recovered content retains up to 98% of the meaning of the original typed content. We also discuss potential defenses.
AB - Virtual Reality (VR) has gained popularity by providing immersive and interactive experiences without geographical limitations. It also provides a sense of personal privacy through physical separation. In this paper, we show that despite assumptions of enhanced privacy, VR is unable to shield its users from side-channel attacks that steal private information. Ironically, this vulnerability arises from VR's greatest strength, its immersive and interactive nature. We demonstrate this by designing and implementing a new set of keystroke inference attacks in shared virtual environments, where an attacker (VR user) can recover the content typed by another VR user by observing their avatar. While the avatar displays noisy telemetry of the user's hand motion, an intelligent attacker can use that data to recognize typed keys and reconstruct typed content, without knowing the keyboard layout or gathering labeled data. We evaluate the proposed attacks using IRB-approved user studies across multiple VR scenarios. For 13 out of 15 tested users, our attacks accurately recognize 86%-98% of typed keys, and the recovered content retains up to 98% of the meaning of the original typed content. We also discuss potential defenses.
UR - https://www.scopus.com/pages/publications/85204320613
UR - https://www.scopus.com/pages/publications/85204320613#tab=citedBy
M3 - Conference contribution
T3 - Proceedings of the 33rd USENIX Security Symposium
SP - 2725
EP - 2742
BT - Proceedings of the 33rd USENIX Security Symposium
PB - USENIX Association
T2 - 33rd USENIX Security Symposium, USENIX Security 2024
Y2 - 14 August 2024 through 16 August 2024
ER -