Secure framework for e health transactions in cloud environment using blockchain

Abstract

The widespread usage of cloud-based e-health systems is increasing at a faster pace due to the advancement in managing Electronic Health Record (EHR), disease analysis, providing prescriptions, etc. But, traditional e-health systems (ordinary and cloud based) are prone to challenges in security during the authentication, storage and data transactions. In this proposed work security is provided in-terms of Authentication via iris and Secure data transactions in Cloud Server through blockchain. One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there is still an abundant problem such as the low match rate of the score level and low accuracy. Therefore, a decision model should be essential for iris recognition systems. In this research work Iris recognition is based on a decision model using a unified framework based on the integration of three detection schemes due to variation occurred in shading and position change using Smallest Univalue Segment Assimilating Nucleus (SUSAN), Generalized Hough Transform (GHT) and Viola Jones (SUSANGHT-VJ) for iris segmentation, enhancing the dimmer and darker areas using fuzzy retinex method and normalizing the iris boundary using Daugman s Rubber Sheet Model for segmentation. Also, the iris corner points are extracted using Gabor Wavelet Transform (GWT), the vector properties of the blurred texture features are quantized using Local Phase Quantization (LPQ) and the optimal decision model based on Atom Search Optimization (ASO) and Feed Forward Counter Propagation Neural Network (FFCNN) for matching score level and classification task. newlineFurthermore, the current framework prevents false matches and inappropriate iris input, thus making the iris match score framework more reliable. The evaluation of the proposed approach is trained and tested with the employed eye template of iris and face datasets. Therefore, the results depict that the proposed technique gives a high recognition rate of 99.9% on different datasets compared to existing methods newline newline