Biometric Identification of Wearable Enterprise AR Device Users

New AR display devices encounter significant resistance from enterprise IT teams who consider the new hardware platforms increase security threats, consequently, increasing the need for an elevated security posture.

Driven by a human-centric approach, a critical step in ensuring compliance with existing security policies and systems is to balance the security with accurate and rapid user authentication and ultra-low-friction user input.

Biometric identification methods, ranging from palm-prints, voice-prints, iris scanning, gait to heartbeat detection offer a plethora of opportunities for identification of wearable enterprise AR device users before providing access to enterprise work orders and data.

This research topic compares different modalities of biometric identification and classifies them based on accuracy, cost and ease-of-use.

Stakeholders

All stakeholder in corporate security organizations but primarily CISOs, CIOs, IT and security managers. On the vendor side, OEMs, solutions providers, system integrators and independent software vendors will be impacted by this research.

Possible Methodologies

This research will require rigorous laboratory tests, deployed via multiple cells of different modalities. This will be followed by human factors and security research, culminating in field trials. Once baselines are available and validated, best practices can be established.

Research Program

This topic is closely related to another proposed AREA Research Agenda topic on cleaning and authenticating multi-user devices end user [ra-Tsecurity5-multiuserdisplays]. The topics could be combined with other AR security topics to develop a broader research program. In addition, the topic could be expanded to use the sensors on devices of other AR users in a workplace to confirm user identities.

Miscellaneous Notes

The field of biometric authentication in cybersecurity is vast and there are many highly reputable research centers that could contribute to this research. Hundreds of publications appear each year in journals and proceedings. This paper describes results of studies to connect AR users with sensitive personal information derived from on-line platforms and use of these data to predict AR user interests and preferences. In the https://dl.acm.org/doi/proceedings/10.1145/3457339[proceedings of the 7th ACM on Cyber-Physical System Security Workshop] (May 2021) an article compiles recently published work on this topic and describes MoveAR. The goal of MoveAR is to distinguish between a legitimate user and potential adversaries based on the signatures detected by the on-device sensors as the user interacts with an augmented reality environment.

Keywords

Biometric, palm print, voice print, gait, retina scanning, iris scanning, heartbeat detection, skin conductivity, access control, data protection, security systems, authentication, message authentication, authorization, data security, access protocols

Research Agenda Categories

Displays, End User and User Experience, Technology

Expected Impact Timeframe

Medium

Related Publications

Using the words in this topic description and Natural Language Processing analysis of publications in the AREA FindAR database, the references below have the highest number of matches with this topic:

• Garae, J., Ko, R. K. L., Kho, J., Suwadi, S., Will, M. A., & Apperley, M. (2017). Visualizing the New Zealand Cyber Security Challenge for Attack Behaviors. 2017 IEEE Trustcom/BigDataSE/ICESS.
• Lebeck, K., Ruth, K., Kohno, T., & Roesner, F. (2018). Towards Security and Privacy for Multi-user Augmented Reality: Foundations with End Users. 2018 IEEE Symposium on Security and Privacy (SP).
• Wazir, W., Khattak, H. A., Almogren, A., Khan, M. A., & Ud Din, I. (2020). Doodle-Based Authentication Technique Using Augmented Reality. IEEE Access, 8, 4022-4034.
• Epstein, J. (2017). An NSF View of Multimedia Privacy and Security. Proceedings of the 2017 on Multimedia Privacy and Security.
• Shen, Y., Wen, H., Luo, C., Xu, W., Zhang, T., Hu, W., & Rus, D. (2019). GaitLock: Protect Virtual and Augmented Reality Headsets Using Gait. IEEE Transactions on Dependable and Secure Computing, 16(3), 484-497.

More publications can be explored using the AREA FindAR research tool.

Author

Peter Orban

Last Published (yyyy-mm-dd)

2021-08-31

Go to Enterprise AR Research Topic Interactive Dashboard