Emergent Mind

Abstract

As IoT becomes omnipresent vast amounts of data are generated, which can be used for building innovative applications. However,interoperability issues and security concerns, prevent harvesting the full potentials of these data. In this paper we consider the use case of data generated by smart buildings. Buildings are becoming ever "smarter" by integrating IoT devices that improve comfort through sensing and automation. However, these devices and their data are usually siloed in specific applications or manufacturers, even though they can be valuable for various interested stakeholders who provide different types of "over the top" services, e.g., energy management. Most data sharing techniques follow an "all or nothing" approach, creating significant security and privacy threats, when even partially revealed, privacy-preserving, data subsets can fuel innovative applications. With these in mind we develop a platform that enables controlled, privacy-preserving sharing of data items. Our system innovates in two directions: Firstly, it provides a framework for allowing discovery and selective disclosure of IoT data without violating their integrity. Secondly, it provides a user-friendly, intuitive mechanisms allowing efficient, fine-grained access control over the shared data. Our solution leverages recent advances in the areas of Self-Sovereign Identities, Verifiable Credentials, and Zero-Knowledge Proofs, and it integrates them in a platform that combines the industry-standard authorization framework OAuth 2.0 and the Web of Things specifications.

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