Secure Blockchain Transactions for Electronic Health Records based on an Improved Attribute-Based Signature Scheme (IASS)

Electronic Health Records (EHRs) are entirely controlled by hospitals, not patients, making it difficult to obtain medical advice from individual hospitals. Patients need to keep tabs on their health details and take back control of their medical data. The rapid development of blockchain technology has facilitated large-scale healthcare, including medical records and patient-related data. The technology provides comprehensive and immutable patient records and free access to electronic medical records for providers and treatment portals. To ensure the validity of the blockchain-connected EHR, the Improved Attribute-Based Signature Scheme (IASS) has considerable powers, allowing patients to approve messages based on attributes but not validated. In addition, it avoids the problem of having multiple authorities without a single or central source of trust for generating and distributing patient public/private keys and fits into the blockchain model for distributed data storage. By sharing a secret, pseudo-random activity seed between authorities, the protocol resists collusive attacks by corrupt officials. The technology provides patients with a comprehensive, immutable record and free access to their EHR from providers and treatment portals. To ensure the validity of blockchain-connected EHRs, propose an attribute-based multi-authority signature scheme that authorizes messages based on their attributes without revealing any information

A Internet of Things Improvng Deep Neural Network Based Particle Swarm Optimization Computation Prediction Approach for Healthcare System

Internet of Things (IoT) systems tend to generate with energy and good data to process and responding. In internet of things devices, the most important challenge when sending data to the cloud the level of energy consumption. This paper introduces an energy-efficient abstraction method data collection in medical with IoT-based for the exchange. Initially, the data required for IoT devices is collected from the person. First, Adaptive Optimized Sensor-Lamella Zive Welch (AOSLZW) is a pressure sensing prior to the data transmission technique used in the process. A cloud server is used data reducing  the amount of data sent from IoT devices to the AOSLZW strategy. Finally, a deep neural network (DNN) based on Particle Swarm Optimization (PSO) known as DNN-PSO algorithm is used for data sensed result model make decisions based as a predictive to make it. The results are studied under distinct scenarios of the presented of the performance for AOSLZW-DNN-PSO method, for that simation are studied under different sections. This current pattern of simalation results indicates that the AOSLZW-DNN-PSO method is effective under several aspects