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Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy
Conference proceeding   Peer reviewed

Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy

Mst Shapna Akter, Hossain Shahriar, Dan Lo, Nazmus Sakib, Kai Qian, Michael Whitman and Fan Wu
2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Vol.2023-, pp.1010-1012
Annual Computers, Software, and Applications Conference (COMPSAC), 47th (Torino, Italy, 06/26/2023–06/30/2023)
07/2023
DOI: https://doi.org/10.1109/COMPSAC57700.2023.00151
Web of Science ID: WOS:001046484100141

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Abstract

Adversarial attack Authentic learning ML/DL algorithm Privacy Security Higher Education Interactive Learning Learning Motivation
The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.

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