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Enhancing Privacy and Security in Online Education Using Generative Adversarial Networks
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Author(s): Gnanasankaran Natarajan (Thiagarajar College, India), Elakkiya Elango (Government Arts College for Women, Sivagangai, India), Ahamed Labbe Hanees (South Eastern University of Sri Lanka, Sri Lanka)and Shirley Chellathurai Pon Anna Bai (Karunya Institute of Technology and Sciences, India)
Copyright: 2024
Pages: 25
Source title:
Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs)
Source Author(s)/Editor(s): Sivaram Ponnusamy (Sandip University, Nashik, India), Jilali Antari (Ibn Zohr Agadir University, Morocco), Pawan R. Bhaladhare (Sandip University, Nashik, India), Amol D. Potgantwar (Sandip University, Nashik, India)and Swaminathan Kalyanaraman (Anna University, Trichy, India)
DOI: 10.4018/979-8-3693-3597-0.ch015
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Abstract
As online education grows in popularity, issues concerning learners' privacy and security have become increasingly important. This chapter delves into the creative use of generative adversarial networks (GANs) to handle the complex difficulties of protecting sensitive information in the online education scene. The chapter opens with a detailed assessment of the present situation of online education. The chapter focuses on the integration of GANs into the online education environment to improve privacy and security. The chapter delves into the technical features of GANs, demonstrating how these networks may be tailored to generate synthetic yet indistinguishable data, reducing the danger of privacy violations. In addition to privacy protection, the chapter investigates the function of GANs in improving the overall cybersecurity posture of online education platforms. Finally, the chapter emphasises Generative Adversarial Networks' transformational potential in altering the privacy and security environment of online education.
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