Treffer: Privacy preserving framework using Gaussian mutation based firebug optimization in cloud computing.

In recent years, the data exchange among the service providers and users has been increased tremendously. Various organizations like banking sectors, health as well as government associations collect and process the data regarding an individual for their beneficial purpose. However, data confidentiality and data privacy are still considered as significant challenges while sharing sensitive data. The cloud storage servers based on unencrypted data are susceptible to both external and internal attacks established by strangers or untrustworthy cloud service providers. Since the medical data are sensitive, the risk based on privacy enhances at the moment of subcontracting entity medical records to the cloud. The significant intention of the proposed approach involves securing and preserving sensitive healthcare data. Here, data hiding and data restoration operations are considered as two significant operations of the proposed framework. Initially, an optimal key is generated in the data hiding operation. This paper proposes a Gaussian mutation-based firebug optimization (GM-FBO) algorithm for the generation of an optimal key. The experiments are conducted using three different healthcare datasets, namely HPD, Medical MIMIC-III, and MHEALTH. The efficiency of the proposed model is compared with different state-of-the-art techniques to determine the efficiency of the system. [ABSTRACT FROM AUTHOR]

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