A secure and extensible blockchain-based data provenance framework for the Internet of Things
Marten Sigwart, Michael Borkowski, Marco Peise, Stefan Schulte, Stefan Tai
TU Wien Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) Technische Universität Berlin
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摘要与影响
As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions for data provenance fail to take the heterogeneous nature of IoT applications and their data into account. In this work, we identify functional and non-functional requirements for a secure and extensible IoT data provenance framework, and conceptualise the framework as a layered architecture. Evaluating the framework using a proof-of-concept implementation based on Ethereum smart contracts, we conclude that our framework can be used to realise data provenance concepts for a wide range of IoT use cases. While blockchain technology generally poses constraints on scalability and privacy, we discuss multiple solutions aiming to overcome these issues.
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计算机 / AIBlockchain Technology Applications and Security
Scientific Computing and Data Management · Privacy-Preserving Technologies in Data
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