Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Conceivable security risks and authentication techniques for smart devices

With the rapidly escalating use of smart devices and fraudulent transaction of users’ data from their devices, efficient and reliable techniques for authentication of the smart devices have become an obligatory issue. This paper reviews the security risks for mobile devices and studies several authentication techniques available for smart devices. The results from field studies enable a comparative evaluation of user-preferred authentication mechanisms and their opinions about reliability, biometric authentication and visual authentication techniques

Data Sharing based on Facial Recognition Clusters

The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a database. Our strategy capitalizes on the dlib library and its face recognition model, which com- bines advanced deep learning methods with traditional computer vision techniques to attain highly accurate face detection and recognition. The essential elements of our system encompass face detection, face recognition, and image retrieval. Initially, we employ the face recognition model to detect and pinpoint faces within the captured image. Following that, we employ facial landmarks and feature embeddings to recognize and match the detected face with entries in a database. Finally, we retrieve and present all images connected to the recognized individual. To validate the effectiveness of our system, we conducted extensive experiments on a diverse dataset that encompasses various lighting conditions, poses, and facial expressions. Our findings demonstrate exceptional accuracy and efficiency in both face detection and recognition, rendering our approach suitable for real-world applications. We envision a broad spectrum of potential applications for our system, including access control, event management, and personal media organization

A First Look Into Users’ Perceptions of Facial Recognition in the Physical World

Facial recognition (FR) technology is being adopted in both private and public spheres for a wide range of reasons, from ensuring physical safety to providing personalized shopping experiences. It is not clear yet, though, how users perceive this emerging technology in terms of usefulness, risks, and comfort. We begin to address these questions in this paper. In particular, we conducted a vignette-based study with 314 participants on Amazon Mechanical Turk to investigate their perceptions of facial recognition in the physical world, based on thirty-five scenarios across eight different contexts of FR use. We found that users do not have a binary answer towards FR adoption. Rather, their perceptions are grounded in the specific contexts in which FR will be applied. The participants considered a broad range of factors, including control over facial data, the utility of FR, the trustworthiness of organizations using FR, and the location and surroundings of FR use to place the corresponding privacy risks in context. They weighed the privacy risks with the usability, security, and economic gain of FR use as they reported their perceptions. Participants also noted the reasons and rationals behind their perceptions of facial recognition, which let us conduct an in-depth analysis of their perceived benefits, concerns, and comfort with using this technology in various scenarios. Through this first systematic look into users’ perceptions of facial recognition in the physical world, we shed light on the tension between FR adoption and users’ concerns. Taken together, our findings have broad implications that advance the Privacy and Security community’s understanding of FR through the lens of users, where we presented guidelines for future research in these directions

Facial Recognition Technology and Ethical Issues

Facial recognition technology (FRT) is being adopted across the world with little thought given to the ethical and sustainability issues it faces. FRT must address these challenges as soon as possible to avoid repercussions. The issues of data privacy, security controls, and accuracy are discussed in this paper. To address the issues, a code of ethics is created and applied to Apple’s Face ID. It is found that Apple has made much progress, but still needs further improvement in its transparency of AI training

Biometrics: Effectiveness and Applications within the Blended Learning Environment

Learning methods have been benefited by a large act of recent systems based on the merging of several models of teaching. Blended learning philosophy has undergone a deep change with the internalization of new engineering sciences such as biometric. While it is known that passwords or PIN should never be stored in the clear, biometric technologies are becoming the foundation of an all-inclusive array of highly secure identification and personal verification solutions. In this paper, we present an in depth discussion the effectiveness of applying different types of biometrics in blended learning environments. We outline an implementation and report the effectiveness of the fingerprint model as a secure biometric method on a database consisting of 13000 students. Keywords: Blended learning, biometrics, e-learning, fingerprint matching, information technology