R
Reza M. Parizi
Researcher at Kennesaw State University
Publications - 168
Citations - 6914
Reza M. Parizi is an academic researcher from Kennesaw State University. The author has contributed to research in topics: Computer science & Blockchain. The author has an hindex of 28, co-authored 146 publications receiving 2890 citations. Previous affiliations of Reza M. Parizi include Taylors University & New York Institute of Technology.
Papers
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Journal ArticleDOI
A survey on security and privacy of federated learning
Viraaji Mothukuri,Reza M. Parizi,Seyedamin Pouriyeh,Yan Huang,Ali Dehghantanha,Gautam Srivastava,Gautam Srivastava +6 more
TL;DR: This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible.
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A systematic literature review of blockchain cyber security
TL;DR: It is shown that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII).
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Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.
TL;DR: A more thorough summary of the most relevant protocols, platforms, and real-life use-cases of FL is provided to enable data scientists to build better privacy-preserved solutions for industries in critical need of FL.
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Blockchain Applications for Industry 4.0 and Industrial IoT: A Review
TL;DR: This paper comprehensively review existing blockchain applications in Industry 4.0 and IIoT settings, and presents the current research trends in each of the related industrial sectors, as well as successful commercial implementations of blockchain in these relevant sectors.
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A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids
TL;DR: The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack.