Adaptive Security Model for Healthcare data using Biomedical NLP
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The security and privacy of patient data has emerged as a critical concern, given the relevance of the qualities of the patient's information in EMR. Unauthorized access and unethical data manipulation could result in a large-scale medical and economic calamity. Many researchers have studied the security of big data over the last decade and developed many security models to protect data from malicious assaults and leakages.The researchers' security and privacy measures, on the other hand, are rigid and homogeneous in nature. However, because of the randomness and dynamism of big data, an adapt...
The security and privacy of patient data has emerged as a critical concern, given the relevance of the qualities of the patient's information in EMR. Unauthorized access and unethical data manipulation could result in a large-scale medical and economic calamity. Many researchers have studied the security of big data over the last decade and developed many security models to protect data from malicious assaults and leakages.The researchers' security and privacy measures, on the other hand, are rigid and homogeneous in nature. However, because of the randomness and dynamism of big data, an adaptive and dynamic security architecture is required to deal with the uncertainty of data properties. Adaptive security is a solution to this challenge that provides a security strategy based on continuously examining behaviour and events and effectively adjusting to dangers before they materialize. The use of machine learning methods to propose an adaptable and dynamic security model in the health-care industry adds a lot of value to the existing security system.
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