A Blockchain-driven Framework for Secure Big Data Analytics through Data Science Integration
Keywords:
Big data analytics, Blockchain, Cybersecurity, Data science, Distributed ledger technology, Federated learning, IoT, Machine learning, Secure data sharingAbstract
The rapid growth of big data technologies has transformed modern industries by enabling large-scale data processing, predictive analytics, and intelligent decision-making. However, centralized big data architectures face major security and privacy challenges, including unauthorized access, data tampering, and a lack of transparency. Blockchain technology offers decentralized, immutable, and transparent mechanisms that can enhance the security and integrity of big data analytics systems. This research article investigates the integration of blockchain and data science techniques for secure big data analytics. A conceptual framework combining distributed ledger technology, machine learning algorithms, and cloud-based big data infrastructure is proposed. The study evaluates the performance of the integrated model in terms of security, scalability, transparency, and analytical efficiency. Experimental findings demonstrate that blockchain integration significantly improves data integrity, authentication, and trust management while maintaining acceptable computational overhead. The study also discusses implementation challenges, future research directions, and real-world applications in healthcare, finance, smart cities, and IoT ecosystems.
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