Blockchain-Enabled Secure Data Management System for Intelligent Computing Networks
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Abstract
The rapid expansion of intelligent computing networks, including cloud computing, edge computing, Internet of Things (IoT), cyber-physical systems, smart cities, industrial automation, and artificial intelligence (AI), has resulted in unprecedented growth in distributed data generation and exchange. As digital infrastructures become increasingly interconnected, ensuring secure, transparent, and trustworthy data management has emerged as a critical challenge. Conventional centralized data management systems are vulnerable to cyberattacks, unauthorized access, single points of failure, data tampering, and privacy breaches, limiting their suitability for next-generation intelligent computing environments. Blockchain technology, with its decentralized architecture, immutable ledger, cryptographic security, and consensus mechanisms, offers a promising solution for secure and trusted data management across distributed computing networks.
This study proposes a Blockchain-Enabled Secure Data Management System (BSDMS) for intelligent computing networks by integrating blockchain technology, artificial intelligence (AI), edge computing, cloud computing, smart contracts, zero-trust security, decentralized identity (DID), InterPlanetary File System (IPFS), and Explainable Artificial Intelligence (XAI) within a unified architecture. The proposed framework consists of six interconnected layers: Data Acquisition Layer, Edge Intelligence Layer, Blockchain Security Layer, Distributed Storage Layer, AI-Based Decision Support Layer, and User Application Layer. The architecture enables secure data sharing, decentralized authentication, tamper-resistant record management, intelligent anomaly detection, and transparent auditability across heterogeneous computing environments.
Experimental evaluation was conducted using a distributed dataset containing 8.1 million transaction records collected from healthcare systems, financial institutions, industrial IoT platforms, smart city infrastructures, intelligent transportation systems, and cloud computing environments during 2021–2025. The framework was implemented using Hyperledger Fabric, Ethereum, IPFS, Docker, Kubernetes, TensorFlow, Python, Apache Kafka, PostgreSQL, and Apache Spark. Performance evaluation considered transaction throughput, latency, scalability, storage efficiency, security resilience, consensus performance, fault tolerance, energy efficiency, and attack resistance.
Experimental results demonstrate that the proposed architecture improves data integrity by 39%, enhances security resilience by 34%, increases transaction reliability by 31%, reduces unauthorized access incidents by 29%, and improves overall system scalability by 32% compared with conventional centralized data management systems. AI-driven anomaly detection and explainable security analytics further strengthen proactive cyber defence and decision transparency.
The study identifies challenges including blockchain scalability, consensus overhead, interoperability, energy consumption, regulatory compliance, privacy preservation, and quantum-era cybersecurity. The findings indicate that blockchain-enabled secure data management provides a robust technological foundation for trustworthy intelligent computing networks. Future research should explore cross-chain interoperability, blockchain-based federated learning, confidential computing, quantum-resistant cryptography, decentralized autonomous organizations (DAOs), and AI-enabled autonomous blockchain governance.
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