Retaining Data Ownership in the Internet of Things

As Internet of Things (IoT) systems further emerge, we face unprecedented security and privacy challenges, especially with regards to the collected data. This data typically consists of sensor readings, tagged with metadata. For scalability, ubiquitous access, and sharing possibilities, the data is most often stored in the cloud. Securing date while in transit and in particular when being stored in the cloud is of utmost importance, as the data can be used to infer privacy-sensitive information. Moreover, transparent and secure data sharing (e.g., sharing with friends or domain experts) is considered a key requirement for the practicality and success of typical IoT systems. In today’s cloud-centric designs, users have no choice but to trust centralized parties. The increased number of security and privacy incidents, such as system compromises or unauthorized trade with users data, show that this trust is not always justified. Despite varying levels of privacy-awareness among users of different age and geopolitical groups, and even societal shifts towards privacy pragmatism and indifference, the security and privacy threats do usually have far-reaching implications, demanding adequate mechanisms and measures to address them. In this dissertation, we investigate building secure IoT systems that protect data confidentiality and retain data ownership. We build secure systems that allow reducing the trust end-users are required to put into third parties within the IoT ecosystem, specifically towards the cloud storage and service providers. More importantly, we take a new approach on empowering the user with ownership and fine-grained access control for IoT data without sacrificing performance or security. In particular, we present three approaches to enabling a secure IoT ecosystem: (i) Talos: Talos is a system that stores IoT data securely in a cloud database while still allowing query processing over the encrypted data. Talos protects data even if the server is compromised. We enable this by encrypting IoT data with a set of cryptographic schemes such as orderpreserving and partially homomorphic encryption. We tailor Talos to accommodate for the resource asymmetry of the IoT, particularly towards constrained IoT devices. We assess the feasibility of Talos on low-power devices with and without cryptographic hardware accelerators and quantify its overhead concerning energy consumption, computation time, and latency. With a thorough evaluation of our prototype implementation,

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