A Privacy-Preserving Trust Management Framework for IoT

dc.contributor.advisorDara, Rozita
dc.contributor.advisorFraser, Evan
dc.contributor.authorAmiri-Zarandi, Mohammad
dc.date.accessioned2023-01-05T20:41:06Z
dc.date.available2023-01-05T20:41:06Z
dc.date.copyright2022-09
dc.date.created2022-10-12
dc.degree.departmentSchool of Computer Scienceen_US
dc.degree.grantorUniversity of Guelphen
dc.degree.nameDoctor of Philosophyen_US
dc.degree.programmeComputational Sciencesen_US
dc.description.abstractThe 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.en_US
dc.identifier.citationM. 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
dc.identifier.urihttps://hdl.handle.net/10214/27373
dc.language.isoenen_US
dc.publisherUniversity of Guelphen
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectInternet of thingsen_US
dc.subjectSurveyen_US
dc.subjectPrivacyen_US
dc.subjectSecurityen_US
dc.subjectMachine Learningen_US
dc.titleA Privacy-Preserving Trust Management Framework for IoTen_US
dc.typeThesisen

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