Abstract
By applying novel way of cloud computing, the computing resources and services are delivered. That can raise the benefit health care research, and alter the face of health information technology. The E-health applications have capable to process the trivial and non-trivial connections among the different sensor signals and big data, better conception of diseases. Cloud computing are controls better advantages but it has also control main security challenges. To raise the data security in cloud computing the two fish encryption algorithm was suggested by us. At first the sensitive data’s are separated into the multiples of data by applying Multi kernel support vector machine (MKSVM) classification algorithm in our article. By applying optimal two fish encryption algorithm, and second the segmented sensitive data are encrypted. Then the encrypted data are saved in the cloud service provider. After the encryption process, the user must be chosen an optimal key. The optimal key will be inquired by the cloud server. For that, we are also suggested an optimization algorithm called as Modified whale optimization algorithm (MWO). After all the verification process only the user found the data from the cloud server. This will increase the security of data cloud computing environment. The performance of our suggested method is computed in terms of accuracy, time and memory utilization. The introduced method is implemented in Cloud sim with JAVA. Our techniques and algorithms are outperforms equated with existing methods.
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07 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04036-z
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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s12652-022-04036-z
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Karuppiah, S.V., Gurunathan, G. RETRACTED ARTICLE: Secured storage and disease prediction of E-health data in cloud. J Ambient Intell Human Comput 12, 6295–6306 (2021). https://doi.org/10.1007/s12652-020-02205-6
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DOI: https://doi.org/10.1007/s12652-020-02205-6