Applying Benford's Law as an Efficient and Low-cost Solution for Verifying the Authenticity of Users’ Video Streams in Learning Management Systems.
Argyris Constantinides, Christodoulos Constantinides, Marios Belk, Christos Fidas, and Andreas Pitsillides. 2021. Applying Benford's Law as an Efficient and Low-cost Solution for Verifying the Authenticity of Users’ Video Streams in Learning Management Systems. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '21). Association for Computing Machinery, New York, NY, USA, 563–569.
An important challenge of online learning management systems (LMS) relates to continuously verifying the identity of students even after they have successfully authenticated. Although various continuous user identification solutions exist, they are rather focused on complex examination proctoring systems. Challenges further increase within large-scale online courses, which require a strong infrastructure to support numerous real-time video streams for verifying the identity of students. Considering that the students’ input video stream is an important factor for verifying their identity, and given that naturally generated data streams have been found to adhere to a pre-defined behavior as indicated by the Benford's law, in this work we investigate whether Benford's law can be applied as a reliable, efficient and cost-effective method for the detection of authentic vs. pre-recorded input video streams during continuous students’ identity verification within online LMS. In doing so, we suggest a prediction model based on the distribution of the first digits of image Discrete Cosine Transform (DCT) coefficients from the students’ input video stream. We found that the input video stream type (authentic vs. pre-recorded) can be inferred within a few seconds in real-time. A system performance evaluation indicates that the suggested model can support up to 1000 concurrent online students using a conventional and low-cost server setup and architecture.Link to paper
Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches.
Christos Fidas, Marios Belk, David Portugal, and Andreas Pitsillides. 2021. Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches. Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, New York, NY, USA, 368–370.
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.
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