A Novel Approach of Smart Contract based Distributed Ledger Technology using Deep Learning Techniques to Secure Medical Images

Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset. © 2022 IEEE.

An efficient lightweight encryption model with re-encryption scheme to create robust blockchain architecture for COVID-19 data

Handling electronic health records from the Internet of Medical Things is one of the most challenging research areas as it consists of sensitive information, which targets attackers. Also, dealing with modern healthcare systems is highly complex and expensive, requiring much secured storage space. However, blockchain technology can mitigate these problems through improved health record management. The proposed work develops a scalable, lightweight framework based on blockchain technology to improve COVID-19 data security, scalability and patient privacy. Initially, the COVID-19 related data records are hashed using the enhanced Merkle tree data structure. The hashed values are encrypted by lattice based cryptography with a Homomorphic proxy re-encryption scheme in which the input data are secured. After completing the encryption process, the blockchain uses inter planetary file system to store secured information. Finally, the Proof of Work concept is utilized to validate the security of the input COVID based data records. The proposed work's experimental setup is performed using the Python tool. The performance metrics like encryption time, re-encryption time, decryption time, overall processing time, and latency prove the efficacy of the proposed schemes. © 2022 John Wiley & Sons Ltd.

A Robust Blockchain Architecture for Electronic Health Data using Efficient Lightweight Encryption Model with Re-Encryption Scheme

The handling of electronic health records (EHRs) from the Internet of Medical Things (IoMT) is one of the most challenging research areas as it consists of sensitive information which is a target for attackers. Also, it is highly complex and expensive to deal with modern healthcare systems as it requires a lot of secured storage space. However, these problems can be mitigated with the improvement in health record management using blockchain technology. To improve data security, patient privacy, and scalability, the proposed work develops a scalable lightweight framework based on blockchain technology. Initially, the COVID-19 related data records are hashed by using an enhanced Merkle tree (EMT) data structure. The hashed values are encrypted by lattice-based cryptography with a Homomorphic Proxy Re-Encryption scheme (LBC-HPRS) in which the input data are secured. After the completion of the encryption process, the blockchain uses IPFS to store secured information. Finally, the Proof of Work (PoW) concept is utilized to verify and validate the security of the input COVID-based data records. The experimental setup of the proposed work is performed by using a python tool and the performance metrics like encryption time, re-encryption time, decryption time, overall processing time and latency prove the efficacy of the proposed schemes. © 2022 IEEE.