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MSc Thesis Defense Announcement by Parth Anand Shukla:"Scaling Private Collaborated Consortium Blockchains Using State Machine Replication over Random Graphs"

Thursday, February 13, 2020 - 10:30 to 12:30

SCHOOL OF COMPUTER SCIENCE

The School of Computer Science is pleased to present…

 

MSc Thesis Defense by:  Parth Anand Shukla

 
Date: Thursday 13th February 2020
Time:  10:30 AM to 12:30 PM
Location: 3105 Lambton Tower
 

Abstract: 

Blockchain technology has redefined the way the software industry's core mechanisms operate. With recent generations of improvement observed in blockchain, the industry is surging ahead towards replacing the existing computing paradigms with consortium blockchain-enabled solutions. For this, there is much research observed which aims to make blockchain technology’s performance at par with existing systems. Most of the research involves the optimization of the consensus algorithms that govern the system. One of the major aspects of upcoming iterations in blockchain technology is making individual consortium blockchains collaborate with other consortium blockchains to validate operations on a common set of data shared among the systems. The traditional approach involves requiring all the organizations to run the consensus and validate the change. This approach is computationally expensive and reduces the modularity of the system. Also, the optimized consensus algorithms have their specific requirements and assumptions which if extended to all the organizations leads to a cluttered system with high magnitudes of dependencies. This thesis proposes an architecture that leverages the use of state machine replication extended to all the nodes of different organizations with seamless updates over a random graph network without involving all the nodes participating in the consensus. This also enables organizations to run their respective consensus algorithms depending on their requirements. This approach guarantees the finality of consistent data updates with reduced computations with high magnitudes of scalability and flexibility.
 

Thesis Committee: 

Internal Reader: Dr. Pooya Moradian Zadeh
External Reader: Dr. Muharem Kianieff
Advisor:  Dr. Saeed Samet
Chair: Dr. Arunita Jaekel

 

 MSc Thesis Defense Announcement

 

5113 Lambton Tower 401 Sunset Ave. Windsor ON, N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca