BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Abstract

Subgraph mining from a large collection of a graph database poses a crucial problem. Our proposed work introduces a Blockchain-based Triune Layered Architecture for authenticated subgraph query search in large-scale dynamic graphs. The two-fold process is handled in BTLA-LSDG: graph indexing and authenticated query search (query processing), which are implemented in triune layers (Data Generation Layer, Data Storage Layer, and Service Layer). Initially, data owners are authenticated to the blockchain using the Four-Q-Curve algorithm. The graph index is constructed by data owners and the merged graph index is constructed by service providers. Based on the uploaded graph index, the hash index is constructed using SHA-3. On the other hand, the data user submits a query with authentication. For every authenticated query, the four-fold process is handled. Firstly, Multi-Constraint-based Belief Entropy function is used for feature set computations for a given query. Then dual similarity-based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Thirdly, a Recurrent Neural Network (RNN) is used for subgraph isomorphic testing. Finally, the graph index refinement process is undertaken to improve the query results. This experiment is implemented using a Hadoop environment and the results show better efficiency in terms of Scalability, Security and Storage. Furthermore, it is tested with Precision, Recall, F-measure, Accuracy, Error Rate, Query Response Time and Positive Results.

KEYWORDS:

• Blockchain
• Hadoop
• Large dynamic graph database
• Subgraph mining
• Structural and semantic similarity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

G. Sharmila

G Sharmila received the BE and ME degrees in computer science and engineering from Madurai Kamaraj University and Anna University, Tamil Nadu, India in 2002 and 2010, respectively. She is pursuing PhD in the Department of Information and Communication Engineering at Anna University, Tamil Nadu, India. She is currently working as an assistant professor at Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India. Her research interests include graph mining, blockchain, cryptography and data security. She is a life Member of the Computer Society of India (CSI) and the Institution of Engineers (IEI) India.

M. K. Kavitha Devi

M K Kavitha Devi received the BE degree in computer science and engineering from Bharathidasan University, Tiruchirappalli, India in 1994 and ME degree in computer science and engineering from Madurai Kamaraj University, Madurai, India in 2004. She received the PhD degree in information and communication engineering from Anna University, Chennai, India in 2011. Her research interests include recommender systems, web intelligence, cryptography & information security, data mining, and data structures. She is currently working as a professor in the Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu, India. She is a life Member of the Association of Computing Machinery (ACM) and Computer Society of India (CSI). She has served as an active reviewer, editor and chair for many reputed international conferences and refereed journals including IEEE, Springer, Elsevier, Inderscience, etc. She has more than 50 publications in international conferences and refereed journals. Email: [email protected]