The rapid advancement in the healthcare sector has led to an exponential increase in the volume of patient data, which necessitates secure and efficient sharing mechanisms. Blockchain technology, renowned for its inherent security features, and deep learning algorithms, capable of processing complex data patterns, present promising solutions to address these challenges. This paper explores the integration of blockchain technology with deep learning to enhance the security and privacy of healthcare data sharing. By leveraging the decentralized nature of blockchain and the predictive power of deep learning, the proposed framework aims to create a robust system that can securely store, manage, and analyze healthcare data. The paper discusses the architecture of the integrated system, highlighting the critical components such as the blockchain ledger, smart contracts, and deep learning models. It also examines the potential benefits and limitations of this approach, focusing on its applicability in real-world scenarios. The findings suggest that the fusion of these technologies can significantly improve the integrity and confidentiality of healthcare data, fostering a more secure and efficient healthcare ecosystem.
Johnson, J. Integrating Blockchain Technology with Deep Learning for Secure Healthcare Data Sharing. Transactions on Applied Soft Computing, 2022, 4, 31. https://doi.org/10.69610/j.tasc.20220819
AMA Style
Johnson J. Integrating Blockchain Technology with Deep Learning for Secure Healthcare Data Sharing. Transactions on Applied Soft Computing; 2022, 4(2):31. https://doi.org/10.69610/j.tasc.20220819
Chicago/Turabian Style
Johnson, John 2022. "Integrating Blockchain Technology with Deep Learning for Secure Healthcare Data Sharing" Transactions on Applied Soft Computing 4, no.2:31. https://doi.org/10.69610/j.tasc.20220819
APA style
Johnson, J. (2022). Integrating Blockchain Technology with Deep Learning for Secure Healthcare Data Sharing. Transactions on Applied Soft Computing, 4(2), 31. https://doi.org/10.69610/j.tasc.20220819
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