Gracy, M, Balasundaram, Rebecca and Raj, S. Albert Antony (2023) Enhancing Data Integrity in Blockchain through Fuzzy Augmented Lagrangian Optimization and Compact Blocks to Minimize Redundancy. International Journal of Intelligent Systems and Applications in Engineering (IJISAE), 11 (4).

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Abstract

Blockchain is a method of storing data that makes it difficult or impossible to modify, steal, or swindle the system. Every block in a blockchain has its header with the unique nonce, timestamp, hash, the previous hash, transaction data, and the Merkle root. The Merkle tree is crucial in a block for consolidating data into a single hash, but it can suffer from data redundancy concerns during its structure formation. The central focus of the paper revolves around data redundancy and presents a novel approach for ensuring data integrity in blockchain with a compactness technique. Compactness is accomplished using Fuzzy Augmented Lagrangian Optimization to reduce data redundancy (FALORR). We integrate compact blocks into regular blockchain setup, bringing out a faster and more efficient way to reduce memory requirements. This effectual transaction verification structure improves the overall security and efficiency of the blockchain network by detecting and preventing malicious activities. To evaluate the effectiveness of the proposed system, we employed Hyperledger Caliper, a specialized benchmarking tool tailored for gauging the performance of blockchain solutions. The results of our implementation and evaluation demonstrate the effectiveness of the proposed structure in minimizing data redundancy and maintaining the data integrity of transactions in the blockchain system.

Item Type:	Article
Status:	Published
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/8979

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