@Article{csse.2023.032127, AUTHOR = {G. Sharmila, M. K. Kavitha Devi}, TITLE = {Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries}, JOURNAL = {Computer Systems Science and Engineering}, VOLUME = {45}, YEAR = {2023}, NUMBER = {2}, PAGES = {1743--1758}, URL = {http://www.techscience.com/csse/v45n2/50433}, ISSN = {}, ABSTRACT = {Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.}, DOI = {10.32604/csse.2023.032127} }