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Poster: Towards Practical Brainwave-based User Authentication

Published: 21 November 2023 Publication History

Abstract

Brainwave measuring devices have transitioned from specialized medical tools to user-friendly and economically accessible consumer products. This shift has opened new avenues for pervasive services, with applications spanning brain-computer interfaces (BCIs), disease detection, criminal trials, and, notably, authentication in computer security. Electroencephalography (EEG) signals, being difficult to steal and revocable, present an attractive biometric option. However, the practical deployment of these signals is hindered by security threats, usability issues, and privacy concerns. To this end, we expect to improve the overall performance of authentication systems using consumer-grade devices, gain a better understanding of user attitudes toward this type of authentication, and protect the user's privacy against unauthorized use of samples collected during enrollment and verification.

References

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Patricia Arias-Cabarcos, Matin Fallahi, Thilo Habrich, Karen Schulze, Christian Becker, and Thorsten Strufe. 2023. Performance and Usability Evaluation of Brainwave Authentication Techniques with Consumer Devices. ACM Transactions on Privacy and Security 26, 3 (2023), 1--36.
[2]
Patricia Arias-Cabarcos, Thilo Habrich, Karen Becker, Christian Becker, and Thorsten Strufe. 2021. Inexpensive Brainwave Authentication: New Techniques and Insights on User Acceptance. In 30th {USENIX} Security Symposium.
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Amir Jalaly Bidgoly, Hamed Jalaly Bidgoly, and Zeynab Arezoumand. 2020. A survey on methods and challenges in EEG based authentication. Computers & Security 93 (2020), 101788.
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John Chuang, Hamilton Nguyen, Charles Wang, and Benjamin Johnson. 2013. I think, therefore I am: Usability and security of authentication using brainwaves. In Financial Cryptography and Data Security: FC 2013 Workshops, USEC and WAHC 2013, Okinawa, Japan. Springer, 1--16.
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Matin Fallahi, Thorsten Strufe, and Patricia Arias-Cabarcos. 2023. BrainNet: Improving Brainwave-based Biometric Recognition with Siamese Networks. In 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 53--60.
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Qiong Gui, Maria V Ruiz-Blondet, Sarah Laszlo, and Zhanpeng Jin. 2019. A survey on brain biometrics. Comput. Surveys 51, 6 (2019), 1--38.
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Barjinder Kaur, Dinesh Singh, and Partha Pratim Roy. 2019. Age and gender classification using brain--computer interface. Neural Computing and Applications 31, 10 (2019), 5887--5900.
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Ivan Martinovic, Doug Davies, Mario Frank, Daniele Perito, Tomas Ros, and Dawn Song. 2012. On the feasibility of side-channel attacks with brain-computer interfaces. In 21st {USENIX} Security Symposium. 143--158.
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Jeff Sauro. 2011. Are both positive and negative items necessary in questionnaires. Retrieved June 20 (2011), 2013.
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Ivo Sluganovic, Marc Roeschlin, Kasper B Rasmussen, and Ivan Martinovic. 2018. Analysis of reflexive eye movements for fast replay-resistant biometric authentication. ACM Transactions on Privacy and Security (TOPS) 22, 1 (2018), 1--30.

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  1. Poster: Towards Practical Brainwave-based User Authentication

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    cover image ACM Conferences
    CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
    November 2023
    3722 pages
    ISBN:9798400700507
    DOI:10.1145/3576915
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 21 November 2023

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    Author Tags

    1. authentication
    2. biometric
    3. eeg
    4. recognition

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    • Helmholtz Association (HGF)
    • Germany?s Excellence Strategy
    • Association (HGF) within topic ?46.23 Engineering Secure Systems?

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    CCS '23
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    Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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