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A Privacy-Preserving Trust Management Framework for IoT

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Title: A Privacy-Preserving Trust Management Framework for IoT
Author: Amiri-Zarandi, Mohammad
Department: School of Computer Science
Program: Computational Sciences
Advisor: Dara, RozitaFraser, Evan
Abstract: The Internet of things (IoT) aims to connect everything and everyone around the world to provide diverse applications that improve quality of life. In this technology, the preservation of data privacy plays a crucial role. Recently, many studies have leveraged Machine Learning (ML) as a strategy to address privacy issues of IoT including scalability, interoperability, and limited resources such as computation and energy. In this study, we aim to review these studies and discuss the opportunities and concerns regarding utilizing data in ML-based solutions for data privacy in IoT. We first, explore and introduce different data sources in IoT and categorize them. Then, we review existing ML-based solutions that are created to protect privacy in IoT. Finally, we examine the extent in which some data categories have been used with ML-based solution to preserve privacy and propose other novel opportunities for ML-based solutions to leverage these data sources in the IoT ecosystem.
URI: https://hdl.handle.net/10214/27373
Date: 2022-09
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
Related Publications: M. Amiri-Zarandi, R. A. Dara, and E. Fraser, “A survey of machine learning-based solutions to protect privacy in the Internet of Things,” Comput. Secur., p. 101921, 2020. DOI: doi.org/10.1016/j.cose.2020.101921


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