Privacy model to secure Chronical big data: Fog Computing
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Abstract
With the tremendous growth in medical data, there are increasing opportunities in the healthcare sector. In this research, the main focus is on providing the current stage of disease and securing sensitive data on the cloud. After taking the patient's input in the form of symptoms of a chronic disease, we are going to make test suggestions. Parameters that are obtained from the results of the tests are mapped to ideal values stored in the database. After mapping a stage of chronic disease, a report will be provided to the patient, and the report will be stored on the cloud in encrypted format. All this data management is done in the cloud, and only solutions are saved there. Fog computing gives fast storage and processing to correspondence benefits near the end client. It is used mainly for reducing latency. Latency is nothing but improvement time. Fog computing is also used to deliver protection by making use of decoy files. The decoy file contains fake or incorrect information that is used to mislead the attacker. It is given to unauthorized access. In this paper, the main focus is on providing the current stage of disease and securing the sensitive data of a patient, which is stored in the cloud. After taking the input in the form of text from the patient, we are going to map this information with ideal values stored in the database. All this data processing is done in the cloud, and only the results are stored there. Fog computing provides storage, processing, and communication services close to the end user. It is used mainly for reducing latency. Healthcare cloud computing faces security issues such as data theft attacks. To decrease the value of these data theft attacks, encryption and decryption are used, and additional security is provided by placing decoy files in the fog. The decoy file contains incorrect information, which we are giving to the attacker after successful decryption
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