Integrating Blockchain and Quantum Key Exchange with Deep Learning for Enhanced Medical Data
Tanvir Habib Sardar, Showkat Ahmad Dar, R. Indhumathi, Gulshan Dhasmana, Guru Prasad M S, Pratik Kumar · Procedia Computer Science · 2025
Medical data security refers to protecting and safeguarding sensitive patient data in healthcare. It encompasses various measures and protocols to ensure medical information’s confidentiality, integrity, and availability. This includes medical imaging data, electronic health records (EHRs), medical test results, patient demographics, and other personally identifiable information (PII). This paper introduces a medical data security framework based on key generation by deep learning-based methods, quantum key exchange, and modified Advanced Encryption Standard AES, which is a deep learning approach for random number generation through encryption keys. Medical data security is important in the following ways. For example, it holds the patient’s data confidential because it won’t allow unauthorised access and disclosure of individual health information, data breaches, and identity theft. This technology generates secure, unpredictable sequences of numbers for encryption keys by using various deep-learning classifiers. Traditionally, random number generators rely on algorithms or some physical process for randomness. It’s another area where deep learning becomes an alternative approach, and its basis is on how much neural networks can be a good learner of the pattern and produce random sequences. The randomly generated number in this protocol is the key in quantum key exchange. It is just a proposed framework using the BB84 protocol and putting down its principles based on the laws of quantum mechanics to ensure that keys would safely be exchanged to keep integrity and confidentiality in transferred data. AES algorithm replaces the conventional mix column operation with a low-complex algorithm for better performance. For medical data applications, the suggested framework offers a novel, hybrid encryption model that outperforms conventional security, effectiveness, and scalability techniques. It guarantees a future-proof solution that can handle both present and upcoming security issues in healthcare by fusing deep learning, quantum key exchange, and AES encryption. A significant advancement in medical data security is represented by the framework’s capacity to defend medical data from quantum threats while preserving high performance and scalability.