An Efficient Data Security in Medical Report using Block Chain Technology

Abstract : ? The health care services industry is always showing signs of change and supporting new advancements and advances. One of the predominant requirements in today's health care systems is to protect the patient's medical report against potential attackers. Hence, it is basic to have secure information that can just approve people can get to the patient's medical report. So, We have proposed Block chain technology as a disbursed approach to grant security in accessing the medical report of a patient. It's composed of three 1. Authentication, 2. Encryption and 3. Data Retrieval using Block Chain technology. For authentication - Quantum Cryptography, for Encryption - AES and for Data Retrieval - SHA algorithms are used to resist the frequent attacks. This proposed framework may likewise ensure the protection of the patients and moreover keeps up the security and trustworthiness of the health care system.

 EXISTING SYSTEM :

 ? The existing systems are poor in processing large volumes of multi-structured healthcare data with less security using AES Algorithum and not providing accurate health recommendation data.

 DISADVANTAGE :

 ? Problem transformation method ?rst transforms one multilabel dataset into multiple single-label datasets, and then exploits existing single-label learning algorithm to process each single-label dataset

 PROPOSED SYSTEM :

 ? The main aim of this research is to provide secure management in accessing the medical records using block chain technology by unique identification of the data security. ? Using Block Chain Crypto System Algorithm the laboratory test data and basic information of patients are Encrypted. ? The clinical dataset is an information system that offers knowledge and personalized information to users in enhancing health and healthcare outcomes .

 ADVANTAGE :

 ? Using Crypto System the data of each patient are secured with crypto currency encryption process. ? A novel framework is proposed for retrieving the most relevant information of patients from multiple data sources,. ? such as laboratory test data, basic information of patients, symptoms of patients and electrocardiogram data, and for combining them to generate integrated features