Blockchain-Integrated AI for Secure and Scalable Big Data Processing

Abstract

The exponential growth of big data in modern enterprises and cloud environments necessitates secure, scalable, and efficient processing techniques. Traditional big data frameworks often struggle with security vulnerabilities, centralized bottlenecks, and computational inefficiencies. To address these challenges, this study proposes a Blockchain-Integrated AI (BCAI) framework that leverages blockchain’s decentralized security and AI-driven optimizations for enhanced big data processing. The framework employs smart contracts for access control, federated learning for distributed model training, and edge computing to reduce latency. Experimental evaluations demonstrate that the proposed BCAI framework improves data integrity, fault tolerance, and processing efficiency while significantly reducing security risks such as data breaches and unauthorized access. Benchmark results on real-world datasets indicate a 35% reduction in processing latency, a 48% increase in security resilience, and higher fault tolerance compared to conventional big data platforms. The findings highlight the potential of blockchainAI convergence in addressing the security and scalability limitations of current big data infrastructures.