skip to main content
10.1145/3450614.3464470acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
invited-talk

Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches

Published:22 June 2021Publication History

ABSTRACT

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data.

References

  1. Mare, S., Baker, M., Gummeson, J. (2016). A Study of Authentication in Daily Life. In Proc. SOUPS 2016, 189-206Google ScholarGoogle Scholar
  2. Ometov, A., Bezzateev, S., Maekitalo, N., Andreev, S., Mikkonen, T., Koucheryavy, Y. (2020). Multi-Factor Authentication: A Survey. Cryptography, 2(1), 1Google ScholarGoogle ScholarCross RefCross Ref
  3. Constantinides, A., Fidas, C., Belk, M., Pietron, A., Han, T., Pitsillides, A. (2021). From Hot-spots towards Experience-spots: Leveraging on Users’ Sociocultural Experiences to Enhance Security in Cued-recall Graphical Authentication. International Journal of Human-Computer Studies, 149Google ScholarGoogle Scholar
  4. Gonzalez-Manzano, L., De Fuentes, J., Ribagorda, A. (2019). Leveraging User-related Internet of Things for Continuous Authentication: A Survey. ACM Computing Surveys, 52(3), article 53, 38 pagesGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  5. Buschek, D., De Luca, A., Alt, F. (2015). Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices. In Proc. ACM CHI 2015, 1393-1402Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Rouhani, S., Deters, R. (2019). Blockchain Based Access Control Systems: State of the Art and Challenges. ACM Web Intelligence, 423-428Google ScholarGoogle Scholar
  7. Jain, A.K., Nandakumar, K., Ross, A. (2016). 50 Years of Biometric Research: Accomplishments, Challenges, and Opportunities. Pattern Recognition Letters, 79, 80-105Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Rui, Z., Yan, Z. (2018). A Survey on Biometric Authentication: Toward Secure and Privacy-Preserving Identification. IEEE Access, 7, 5994-6009Google ScholarGoogle ScholarCross RefCross Ref
  9. Bhalla, A. (2020). The Latest Evolution of Biometrics. Biometric Technology Today, 2020 (8), 5-8Google ScholarGoogle ScholarCross RefCross Ref
  10. Gray, S.L. (2017). Biometrics in Schools: The Role of Authentic and Inauthentic Social Transactions. BSA Conference 2017Google ScholarGoogle Scholar
  11. Pagnin, E., Mitrokotsa, A. (2017). Privacy-Preserving Biometric Authentication: Challenges and Directions. Security and Communication Networks, 2017, 7129505Google ScholarGoogle ScholarCross RefCross Ref
  12. Labati, R.D., Genovese, A., Muñoz, E., Piuri, V., Scotti, F., Sforza, G. (2016). Biometric Recognition in Automated Border Control: A Survey. ACM Computing Surveys, 49(2), Article 24, 39 pagesGoogle ScholarGoogle Scholar
  13. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J. (1997). Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Guo, G., Zhang, N. (2019). A Survey on Deep Learning based Face Recognition. Computer Vision and Image Understanding, 189, 102805Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Schroff, F., Kalenichenko, D., Philbin, J. (2015). Facenet: A Unified Embedding for Face Recognition and Clustering. IEEE Conference on Computer Vision and Pattern Recognition, 815-823Google ScholarGoogle Scholar
  16. Constantinides, A., Belk, M., Fidas, C., Pitsillides, A. (2020). An Eye Gaze-driven Metric for Estimating the Strength of Graphical Passwords based on Image Hotspots. ACM Intelligent User Interfaces (IUI 2020), ACM Press, 33-37Google ScholarGoogle Scholar
  17. Constantinides, A., Belk, M., Fidas, C., Pitsillides, A. (2019). On the Accuracy of Eye Gaze-driven Classifiers for Predicting Image Content Familiarity in Graphical Passwords. ACM User Modeling, Adaptation and Personalization (UMAP 2019), ACM Press, 245-249Google ScholarGoogle Scholar
  18. Boles, A., Rad, P. (2017). Voice Biometrics: Deep Learning-based Voiceprint Authentication System. System of Systems Engineering Conference, IEEE, 1-6Google ScholarGoogle Scholar
  19. Nagrani, A., Chung, J.S., Xie, W., Zisserman, A. (2020). Voxceleb: Large-scale Speaker Verification in the Wild. Computer Speech & Language, 60, 101027Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Ravanelli, M., Bengio, Y. (2018). Speaker Recognition from Raw Waveform with Sincnet. IEEE Spoken Language Technology Workshop, IEEE, 1021-1028Google ScholarGoogle ScholarCross RefCross Ref
  21. Belk, M., Portugal, D., Christodoulou, E., Samaras, G. (2015). Cognimouse: On Detecting Users’ Task Completion Difficulty through Computer Mouse Interaction. ACM Conference Extended Abstracts on Human Factors in Computing Systems, 1019-1024Google ScholarGoogle Scholar
  22. Zhang, R., Xue, R., Liu, L. (2019). Security and Privacy on Blockchain. ACM Computing Surveys, 52(3), article 51Google ScholarGoogle Scholar
  23. Tran, Q.N., Turnbull, B.P., Wu, H., de Silva, A., Kormusheva, K., Hu, J, (2021). A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture. IEEE Open Journal of the Computer Society, 2, 72-84Google ScholarGoogle ScholarCross RefCross Ref
  24. Sarier, N.D. (2018). Privacy Preserving Biometric Identification on the Bitcoin Blockchain. International Symposium on Cyberspace Safety and Security (CSS 2018), 254-269Google ScholarGoogle Scholar
  25. Tran, Q.N., Turnbull, B.P., Hu, J. (2021). Biometrics and Privacy-Preservation: How Do They Evolve? IEEE Open Journal of the Computer Society, 2, 179-191Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
              June 2021
              431 pages
              ISBN:9781450383677
              DOI:10.1145/3450614

              Copyright © 2021 Owner/Author

              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.

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 22 June 2021

              Check for updates

              Qualifiers

              • invited-talk
              • Research
              • Refereed limited

              Acceptance Rates

              Overall Acceptance Rate162of633submissions,26%

              Upcoming Conference

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            HTML Format

            View this article in HTML Format .

            View HTML Format