Retaining Data Ownership in the Internet of Things

Abstract

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 order- preserving 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, we show that Talos is a practical system that can provide a high level of security with reasonable overhead. (ii) Pilatus: Storage of data on cloud services naturally facilitates data sharing with third-party services and other users, but bears privacy risks. We present Pilatus, a data protection platform that extends Talos where the cloud stores only encrypted data, yet is still able to process a defined set of database queries (e.g., range or sum). Pilatus features a novel encrypted data sharing scheme based on re-encryption, with revocation capabilities and in situ key-update. Our solution includes a suite of novel techniques that enable efficient partially homomorphic encryption, decryption, and sharing. We present performance optimizations that render these cryptographic tools practical for mobile platforms. We implement a prototype of Pilatus and evaluate it thoroughly. Our optimizations achieve a performance gain within one order of magnitude compared to state-of-the-art realizations. (iii) Droplet: Droplet is a secure data management system that we designed from the ground up to accommodate for the distributed nature of the IoT and revive the IoT from the current vertical design paradigm. The consequent myriad of isolated data silos of classical vertical architectures is hard to manage and prevent heterogeneous applications from interacting with our IoT data. To address this challenge, we leverage the blockchain technology to bootstrap trust for a distributed, secure, and resilient access control and data management scheme. Droplet handles time series data, enables reliable sharing among heterogeneous applications without intermediate trust entities, and features a cryptographically-protected fine-grained and scalable access control mechanism to data streams. We leverage a hash-chain-based key management mechanism to enable interval sharing and compact key distribution. The built-in cryptocurrency feature of blockchains allows the integration of economic incentives into our system. These properties enable a variety of applications that are presently not easily realizable using existing systems. The systems proposed and discussed in this dissertation demonstrate that end-to-end encryption with secure sharing can be achieved in IoT ecosystems with a modest overhead, while maintaining a consistent user- experience.