Dingo Energy Valley Optimization Based Deep Learning for Classification of Privacy Preserved Medical Data in Cloud
Keywords:
Dingo Energy Valley Optimization, Deep Neuro Fuzzy Network, privacy preserved data, cloud computing, deep learningAbstract
In cloud, data security is a major concern while storing and retrieving data. Lately, the growing challenges include handling sensitive data and the model’s privacy. Data classification techniques are employed for providing data security and it categorizes the data depending on their sensitivity. In this paper, data classification for privacy-preserved medical data is performed via employing a novel secure technique Dingo-Energy Valley Optimization- Deep Neuro Fuzzy Network (DEVO- DNFN). Here DEVO is developed by integrating Energy Valley Optimizer (EVO) as well as Dingo Optimizer (DOX). Initially, the cloud simulation is carried out and from the dataset the input medical data is attained. Subsequently, the cloud data privacy is preserved with generation of a privacy utility coefficient matrix using DEVO based on deep learning. The privacy-preserved data is then stored up in the cloud, thus the same key is required by the third party for the data retrieval. Here, DNFN is employed for the classification, which is then tuned with DEVO. The DEVO- DNFN for classification of medical data is investigated using diverse evaluation measures, such as, True Negative Rate (TNR), True Positive Rate (TPR) and accuracy is observed to attain values of 0.912, 0.907, and 0.915 correspondingly.
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