Sparse Attention-based BiLSTM with Blockchain-Enabled Friendly Hashing Encryption Technique for Robust and Secure Data Exchange in Healthcare Sector

The incorporation of Artificial Intelligence (AI) has made the Information and Communication Technology (ICT) to face numerous quick fluctuations. These modifications have highly impacted the regions of management, communication, resource organization, end-to-end device communication, and the application-oriented ICT aspects. The relation of distinct end systems, especially in the interoperable space, privacy protection, integrity, provenance, security, and huge amounts of everyday media produced in the advanced healthcare setting can also pose problems. To address these issues, a Blockchain with a deep learning-assisted secure data management scheme is implemented in this research to make secure data exchange in the healthcare sector. The data collection is initially fetched from a public online source. The Sparse Attention-based Bidirectional Long Short Term Memory (SA-BiLSTM) is applied to securely exchange the data among the interconnected nodes by eliminating the malicious nodes. The sensitive data of the patients is securely stored in the Blockchain by applying encryption over the medical data using Hyper Elliptic Curve Cryptography with Friendly Hashing (HECC-FH) to provide maximum security in the healthcare sector. Finally, experimental evaluation is conducted to validate the effectiveness of the implemented secure data storage approach in the healthcare domain using diverse measures.