Abstract
This chapter aims to show that our research using the Hyperledger Fabric blockchain for Navy logistics assets can be applied to data supporting artificial intelligence (AI) and software development in terms of system safety and timely data acquisition. Data-driven AI/machine learning (ML) requires trusted data for their use in AI functions and requires significant amounts of training data from diverse sources including Internet of things (IoT) devices/sensors. Unauthorized alterations to data supporting AI/ML could go unnoticed within the AI function build process but surface during operation in hazards affecting unwanted human death or resource destruction. AI/ML controlling hardware usually falls into the two highest software control categories: Levels 1 and 2, risk of death, disability, or resource destroyed. We show how trust can be implemented through distributed consensus to ensure that only authorized people can modify data and that the modification is traceable and transparent. Distributed ledgers provide system safety through BC provenance, immutability, and policy enforcement through smart contracts. We also explain how Hyperledger Fabric could be used to provide data to researchers in a timely manner through “smart repositories” while ensuring provenance and integrity.
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The team was given access to the Oracle Cloud Platform thanks to the NPS liaison relationship with the Oracle Blockchain team.
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Kendall, A., Das, A., Nagy, B., Johnson, B., Ghosh, A. (2022). Using Hyperledger Fabric Blockchain to Improve Information Assurance of IoT Devices for AI Model Development. In: Maleh, Y., Tawalbeh, L., Motahhir, S., Hafid, A.S. (eds) Advances in Blockchain Technology for Cyber Physical Systems. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-93646-4_11
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