Dr. Yanjun Zuo, Professor of Accountancy, recent papers accepted and grant award
Please join us in congratulating Dr. Yanjun Zuo, Professor of Accountancy for his recent publications & research grant award!
Title: “Making Smart Manufacturing Smarter – a Survey on Blockchain Technology in Industry 4.0″
Journal: Enterprise Information Systems (JQL Level 3)
Authors: Dr. Yanjun Zuo
Abstract: Blockchain is distributed digital ledger technology that facilitates reliable and transparent transactions among the participants of a system. As a disruptive and promising technology, blockchain provides a trusted platform for decentralized decision making, secure communications, tamper-resistant transaction recording, and reliable integrations among the participants of a network. Blockchain has been applied to various manufacturing applications including Industrial Internet of Things, machine maintenance and automation, additive manufacturing, cloud manufacturing, data management and analysis, manufacturing supply chain, and smart energy supply. Blockchain helps to create an automatic, decentralized smart manufacturing system with a high level of efficiency and productivity. This paper presents a comprehensive survey on blockchain in Industry 4.0 – applications, architectures, techniques, and research challenges. We propose a blockchain reference architecture for smart manufacturing, which guides our discussions on applying the blockchain technology to various applications of the smart factory and smart supply chain.
Title: “Big Data and Big Risks: a Four-factor Framework for Big Data Security and Privacy”
Journal: International Journal of Business Information Systems (JQL Level 2)
Authors: Dr. Yanjun Zuo
Abstract: Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organizations and individuals by improving productivity and enriching people’s life. However, security and privacy are big concerns for big data applications. While a large quantity of data is collected, securely storing, processing and using the data are challenging. In this paper, we propose a four-factor framework for big data security and privacy in business information systems. The proposed framework addresses big data security and privacy issues in terms of collecting the right data, collecting the right amount of data, protecting the data in the right way, and using the data for the right purposes. We present a set of approaches and models for each of the four factors to improve big data security and privacy.
In October 2020, Dr. Zuo nd Dr. Hui Pu (Assistant Professor in the Department of Petroleum Engineering at UND) were awarded by ND EPSCoR a research grant in the amount of $10,000 to conduct data to conduct research, “Study on the Application of Industrial Internet of Things to Optimize Oil Wells’ Production”.