Privacy-Preserved Internet of Things

The Internet of Things (IoTs) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of smart applications. Although the privacy of such wireless applications has been thoroughly studied in Wireless Sensor Networks (WSNs), IoT applications face emerging challenges and potential privacy risks due to unique IoT characteristics. These risks endanger the vision of the next generation ubiquitous Internet and the proliferation of the IoT. This dissertation focuses on addressing data privacy leakage issues involved in providing data storage and publishing, and machine learning services. Three common IoT functionalities are mainly focused respectively, which are client-to-server private data storage and acquisition, server-to-client private data publishing, and privacy-preserving machine learning services on server-side. The privacy leakage during these services will cause tremendous threats to normal users. Different to the solutions under the traditional domain, this dissertation aims to provide and discuss potential solutions under strict resource constraints.