A novel blockchain-based clustering model for linked open data storage and retrieval

Area of Science:

• Computer Science
• Data Science
• Information Technology

Background:

• Organizations increasingly use blockchain for open data sharing.
• Blockchain-based open data models face challenges in scalability, access, and privacy.

Purpose of the Study:

• Introduce a novel Blockchain-based Clustering Model for Linked Open Data Storage and Retrieval (BCLOD).
• Address scalability, access, and privacy challenges in blockchain-based open data systems.

Main Methods:

• Organize network nodes into clusters.
• Group transactions into cluster-specific linked blocks with partial block structures.
• Implement partial and full chains for enhanced scalability and trustworthiness.
• Utilize a two-layer Role-Based Access Control (RBAC) mechanism for privacy.

Main Results:

• Demonstrated significant reduction in storage space for partial and full chains compared to traditional blockchains.
• Prevented fork occurrences.
• Successfully mitigated attacks including Sybil, Distributed Denial of Service (DDoS), and Eclipse attacks.

Conclusions:

• BCLOD effectively addresses key challenges in blockchain-based open data storage and retrieval.
• The model offers improved scalability, enhanced privacy, and robust security.
• BCLOD presents a viable solution for secure and efficient linked open data management.