Role of Machine Learning on Key Extraction for Data Privacy Preservation of Health Care Sectors in IoT Environment

P. N. Kathavate
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Abstract

Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.
机器学习在物联网环境下医疗保健部门数据隐私保护密钥提取中的作用
医疗领域的隐私和安全是当今时代需要考虑的主要方面。这是由于提供者、支付者和患者分别对数据的巨大需求。近年来,越来越多的研究者从不同的角度对这一领域做出了自己的贡献。但是,在安全性方面还需要更多的增强。在这种情况下,本文拟提出一种新的基于物联网的医疗保健隐私保护模型,并结合机器学习算法的影响。在这里,来自物联网设备的信息或数据通过生成最佳密钥受到提议的消毒过程的影响。在这项工作中,机器学习模型的效用是生成最优键的最大途径,因为它已经被神经网络训练过了。其中,最优密钥的识别是最大的危机,这是由一种新的改进蜻蜓算法来完成的。因此,清理过程工作,最后,清理后的数据被上传到物联网。数据恢复是卫生处理的逆过程,即恢复原始数据。最后,在消毒和恢复分析方面,通过最先进的模型验证了所提出工作的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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