Blockchain-Assisted Secure Data Sharing Framework for Electronic Health Records in Smart Hospitals
Abstract
Electronic Health Records (EHRs) play a vital role in modern healthcare by enabling efficient patient information management. However, centralized storage architectures remain vulnerable to unauthorized access, data manipulation, and privacy breaches. This paper presents a blockchain-assisted framework for secure EHR management that combines permissioned blockchain technology, smart contracts, and decentralized identity management. The proposed architecture allows healthcare providers to securely access patient records while maintaining data integrity and auditability. Attribute-based encryption and role-based access control ensure that only authorized users can retrieve sensitive information. Experimental analysis demonstrates improvements in transaction security, data availability, and access efficiency while maintaining acceptable computational overhead. The proposed framework enhances trust, transparency, and interoperability in smart hospital environments.
References
AI-Driven Predictive Analytics for Sustainable Smart City Development. (2026). International Conference on Data Science, Data Engineering, Security & Healthcare for Sustainable Development 2026 , 1(1). https://publication.huntfortalent.org/index.php/book2/article/view/15
BIG DATA ANALYTICS FOR SUSTAINABLE AGRICULTURE, ENERGY, AND ENVIRONMENTAL MONITORING. (2026). International Conference on Data Science, Data Engineering, Security & Healthcare for Sustainable Development 2026 , 1(1). https://publication.huntfortalent.org/index.php/book2/article/view/19
Kurra, R. K., & Molli, S. M. (2026). AI in nutrition, dental, and healthcare: Transforming dietary science for comprehensive well-being. In Modernizing the Food Industry: AI-Powered Infrastructure, Security, and Supply Chain Innovation (pp. 305-328). IGI Global Scientific Publishing.
Kurra, R. K., & Molli, S. M. (2026). Safe and Efficient Autonomous Driving via Deep Reinforcement Learning with Safety-Constrained Soft Actor-Critic. Research Digest on Engineering Management and Social Innovations (p-ISSN: 3117-4418 e-ISSN: 3117-4426), 2(3), 1-11.
Molli, S. M. (2026). Resource-Aware GPU Scheduling for Large-Scale Foundation Model Training in Hybrid Cloud Environments. International Journal of Science, Technology and Convergence, 8(8).
Molli, S. M. (2025). Mitigating Hallucinations in Large Language Models through Multi-Agent Verification and AI Guardrails. International Journal of Science, Technology and Convergence, 7(7).
Konda, P. R. (2026). Cloud-Native AI/ML Analytics Platform for Real-Time Enterprise Data Processing and Optimization. Synergia: A Journal of Multidisciplinary Innovation, 8(8). Retrieved from https://ijcdra.us/index.php/Synergia/article/view/72
Sharma, M., Vangara, Y., Sharma, P., & Konda, P. R. (2025, June). NeuroNav: A Hybrid Deep Learning Framework for Sustainable Autonomous Indoor Robot Localization and Navigation. In International Conference on Sustainable Development through Machine Learning, AI and IoT (pp. 330-349). Cham: Springer Nature Switzerland.
Konda, P. R. (2025). ADVANCED ENTERPRISE DATA ENGINEERING USING MACHINE LEARNING AND SCALABLE CLOUD ARCHITECTURES. Indonasian Journal of Advanced Research & Technology , 7(7). Retrieved from https://scholarlyarticle.vncinstitute.com/index.php/IJART/article/view/71
Konda, P. R. (2024). AI-DRIVEN CLOUD DATA ANALYTICS FRAMEWORK FOR INTELLIGENT ENTERPRISE DECISION SYSTEMS. Indonasian Journal of Advanced Research & Technology , 6(6). Retrieved from https://scholarlyarticle.vncinstitute.com/index.php/IJART/article/view/70
