Barker, KenOnu, Emmanuel2022-04-062022-04-062022-03Onu, E. (2022). Personalized privacy preservation in IoT (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/114536The widespread use and deployment of the Internet of Things (IoT) devices have been instrumental in automating many of our everyday tasks. Its ability to seamlessly integrate and improve the activities in our daily lives has created a wide application for it in several domains, such as smart buildings and cities. However, despite the numerous benefits associated with the integration of the IoT, there are some privacy challenges. These privacy challenges result from the ability of IoT devices to pervasively collect data about their surroundings, which could reveal sensitive information. Though the data may be collected for genuine purposes such as service personalization, previous research has identified two fundamental causes of privacy concern with data collection: 1) the lack of awareness of the presences and practices of data collecting IoT devices, and 2) the lack of control over data collected by these devices. Current efforts to address the issue of privacy awareness raise a new problem of how to deal with the cognitive burden associated with making several privacy decisions across different contexts. In addition, very little work has developed approaches for giving users control over their privacy in a smart environment. To address the privacy challenges with the IoT, it is vital to create a privacy-sensitive smart environment. A core tool required for such an environment is an intelligent personalized privacy assistant that will mediate the interactions between users and IoT devices around them. Some of the essential requirements for this privacy assistant include notification about data collecting IoT devices, user preference capturing, and privacy recommendations. In this research, we focus on some of the vital requirements for this privacy-preserving smart environment, which include IoT privacy policy modeling, user preference evaluation, user privacy preference prediction, and privacy contract negotiation. Privacy policy modeling is essential for creating privacy awareness and capturing users' preferences. We present important privacy dimensions that should be contained within an IoT privacy policy. Additionally, an understanding of people's privacy preferences is key to giving them control over their privacy and creating a more privacy-sensitive environment. We propose a workflow for analyzing three key preferences of people in an IoT environment: Notification, Control, and Permission. Furthermore, we offer a novel approach for predicting people's privacy preferences using a hybrid of Knowledge-based and Collaborative Filtering (CF), an approach commonly employed in recommender systems. Our approach is based on the premise that people share similar privacy preferences. Therefore, we predict the privacy decisions of a person by considering the privacy decisions made by people who are like them and have made privacy decisions in a similar context. The semantic similarity between two IoT contexts is established through the help of a taxonomy defined over each variable that composes the context. We then evaluate the efficiency of our approach using a dataset that contains the privacy preferences of 172 participants obtained in a simulated campus-wide IoT environment. Finally, we present a privacy contract negotiation protocol for the IoT based on the infrastructures in our privacy-preserving smart environment framework.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.PrivacyIoTRecommender systemsPrivacy preferencesPrivacy policyPrivacy negotiationPrivacy preserving smart environmentPrivacy recommendationComputer Sciencedoctoral thesis10.11575/PRISM/39678