dc.contributor.advisor | Rao, Praveen R. | |
dc.contributor.advisor | Allsworth, Jenifer E. | |
dc.contributor.author | Bhagavan, Srini | |
dc.date.issued | 2021 | |
dc.date.submitted | 2021 Fall | |
dc.description | Title from PDF of title page viewed March 30, 2022 | |
dc.description | Dissertation advisors: Praveen Rao and Jenifer Allsworth | |
dc.description | Vita | |
dc.description | Includes bibliographical references (pages 112-124) | |
dc.description | Thesis (Ph.D.)--School of Computing and Engineering, School of Medicine. University of Missouri--Kansas City. 2021 | |
dc.description.abstract | With increased awareness comes unprecedented expectations. We live in a digital,
cloud era wherein the underlying information architectures are expected to be elastic, secure,
resilient, and handle petabyte scaling. The expectation of epic proportions from the
next generation of the data frameworks is to not only do all of the above but also build it
on a foundation of trust and explainability across multi-organization business networks.
From cloud providers to automobile industries or even vaccine manufacturers, a typical
supply chain consists of complex networks of suppliers, manufacturers, distributors, retailers,
auditors, and consumers. With cloud providers, even though there is an increased
focus on self-service or cloud provider-managed SaaS (Software-as-a-service), a portion
of sales for an enterprise customer occurs the old-fashioned way with the sales department
drawing up a purchase order for the procurement process. In many cases, there
could be several disjoint, not fully digitized strings of suppliers behind the scenes. This leaves the buyer unbeknownst and unaware of the state of their order in real-time as it
is challenging to build machine learning and AI-based systems for order fulfillment, time
and cost predictive models, share data transparently and issue remediations when multiple
organizations are involved to fulfill an order.
In this dissertation, motivated by challenges in the industry, we propose a decentralized
distributed system that can be considered as a building block for supply chain
infrastructures, regardless of industry. The design goal of our system is to streamline
complex non-repudiated transaction workflows by efficient handling of enterprise-scale
purchase orders. We present transparent alternatives in real-time to customers based on
model inference to respond to prediction requests. To further support this, we build
a recommendation system model (Matrix Factorization) that is trained using Federated
Learning on an Ethereum blockchain network. We leverage smart contracts that allow
decentralized serverless aggregation to update localized items vectors. Furthermore, we
utilize Homomorphic Encryption (HE) to allow sharing the encrypted gradients over the
network while maintaining their privacy. Based on our results, we argue that training a
model over a serverless Blockchain network using smart contracts will provide the same
accuracy as in a centralized model while maintaining our serverless model privacy and reducing
the overhead communication to a central server. Finally, we assert such a system
that provides transparency, audit-ready and deep insights into supply chain operations for
enterprise cloud customers resulting in cost savings and higher Quality of Service (QoS). | |
dc.description.tableofcontents | Introduction -- Background -- Ethereum and smart contracts -- Related work -- Approach -- Implementation and evaluation -- Conclusion and future work -- Appendix | |
dc.format.extent | xv, 126 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/89572 | |
dc.subject.lcsh | Blockchains (Databases) | |
dc.subject.lcsh | Cloud computing | |
dc.subject.lcsh | Smart contracts | |
dc.subject.lcsh | Supply chain | |
dc.subject.other | Dissertation -- University of Missouri--Kansas City -- Computer science | |
dc.subject.other | Dissertation -- University of Missouri--Kansas City -- Medicine | |
dc.title | FedSmarteum: Secure Federated Matrix Factorization Using Smart Contracts for Multi-Cloud Supply Chain | |
thesis.degree.discipline | Computer Science (UMKC) | |
thesis.degree.discipline | Biomedical and Health Informatics (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Doctoral | |
thesis.degree.name | Ph.D. (Doctor of Philosophy) | |