Toward Data Privacy Related To The Internet of Things

In the past years, the number of Internet of Things (IoT) devices has dramatically grown. These IoT devices are equipped with one or more sensors, and connected to the Internet to make them significantly beneficial and attractive to the user. In addition, service providers use these IoT devices to collect users\u27 personal data to provide them with satisfied services and personalized experience. However, using IoT devices to share personal data with the service providers could introduce many privacy risks to users. In this dissertation, we study privacy issues related to the IoT, and we introduce new schemes to enhance the users\u27 privacy when the users use IoT devices. First, we propose a privacy-preserving data query scheme in the home IoT voice system, which enables users to use voice commands for uploading voice data and later retrieving them securely. Second, we propose an efficient privacy-preserving IoT contact tracing scheme for infection detection scheme (EPIC), which enables users to check if they have ever got in contact with an infected user in the past. EPIC employees short-range wireless IoT devices to perform the contact tracing task in a privacy manner. Third, we propose a scheme that supports posting and searching protocols for sharing IoT location-based comments. This scheme allows users to share location-based comments without a need to disclose a user\u27s location to service providers, the user\u27s location represents a physical location for an IoT device. Finally, we evaluate the performances of our proposed schemes by conducting intensive real-world experiments, and we show that our proposed schemes can achieve the privacy, accuracy, and efficiency objectives