Secure Medical Data Transmission In Iot Healthcare: Hybrid Encryption, Post-Quantum Cryptography, And Deep Learning-Enhanced Approach
D Karthikeyan · 2023
In the realm of IoT healthcare, safeguarding medical data is of paramount importance. This study introduces a comprehensive approach that combines hybrid encryption, post-quantum cryptography, and deep learning techniques for secure medical data transmission. The process begins with data transformation, where collected medical data is converted into binary form and categorized into odd and even positions. Following that, hybrid encryption is used, combining RSA and Two fish for the odd positions, and advanced post-quantum cryptography, specifically NTRU Encrypt, for the even bits. Key selection leverages a hybrid deep learning model, incorporating Bi-LSTM and CNN. The resulting ciphertexts from RSA-Twofish and NTRU Encrypt are merged using secure multiparty computation. In parallel, a region of non-interest extraction employs an optimized U-Net with an anchor box optimized through the Self-Improved Butterfly Optimization Algorithm (SI-BOA). Advanced lattice-based coding further secures the ciphertext, making data inference computationally infeasible. Hyperelliptic curve cryptography is utilized for watermark embedding into medical images to ensure data confidentiality and integrity. Image compression via High-Efficiency Video Coding (HEVC) minimizes storage requirements without compromising image quality. Secure cloud storage is employed, complemented by blockchain integration to enhance data integrity, auditability, and access control. At the receiving end, a reverse process is executed to retrieve and decrypt medical data securely. This multifaceted approach offers a robust solution for the IoT-based delivery of healthcare of secure medical data.