A SECURE ELECTRONIC HEALTH FRAMEWORK TO PROTECT HEALTH RECORDS USING NATURAL LANGUAGE PROCESSING WITH MULTI LEVEL DATA ENCRYPTION IN CLOUD - N. Subhalakshmi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Big data is a set of a massive quantity of large datasets with data volume. With the growing number of data, the demand for big data storage will increase. By setting the records inside the cloud, that data is to be available to anybody from anywhere. Cloud computing is an evolving, carrier-centric framework for performing distributed and parallel computing on large datasets. As the benefits of cloud computing increase in terms of cost, storage space, and scalability, all data providers and institutions are also focusing on offloading data from local servers to remote cloud servers. Medical records are essential and most important because the government retains additional data on the medical history of the data and medical professionals can provide the most appropriate and effective remedies or support for their concerns. It is also useful for diagnosing viable illnesses, identifying family hereditary and possible illnesses, allergic reactions, past and present dosing, and vaccination statistics. The proposed work aims to develop a three-tier framework to protect the privacy of records stored in big data environment and analyses the document about the protected text and breaks the protected content into separate documents. This research work categorizes, distributes, and stores health-related content using a combination of Natural Language Processing (NLP) and text mining algorithms. After associating the distributed content with the original parent document, it encrypts the attribution information of the patient's history and saves in the clouds for future