Abstract
The application of IoT in the several fields is extending due to its simple and robust functionality. Especially, with the advent of the IoT-cloud based devices, IoT are established in the field that process very high amount of data. The health care system is one of the emerging applications of the IoT-Cloud. Many research works are carried out in ensuring the privacy of the patient data. The main issues in the IoT-cloud based health system remains on the security of data along with computation overheads. Predicting disease using patient data from the IoT device is another demanding aspect of health systems. In this research work, a novel Homomorphic Encryption with Random Diagonal Elliptical curve cryptography integrated with Multi-nomial smoothing Naive Bayes (HERDE-MSNB) is proposed to provide the effective security and predict the disease over patient data in the IoT Health Cloud system. The cryptic framework in the proposed architecture involves the encryption and decryption of the patient data along with key words through the HERDE algorithm. The Medicinal person deciphers the encrypted data and performs the prediction through the MSNB model. The UCI repository dataset is employed to predict the performance of the security and prediction model. From the analysis it is observed that the proposed architecture is effective in providing security and disease prediction than the existing models with less processing time, computational cost and increased accuracy. The future work may include all aspects of the dataset with robust prediction model.
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20 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04173-5
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04173-5
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Vedaraj, M., Ezhumalai, P. RETRACTED ARTICLE: HERDE-MSNB: a predictive security architecture for IoT health cloud system. J Ambient Intell Human Comput 12, 7333–7342 (2021). https://doi.org/10.1007/s12652-020-02408-x
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DOI: https://doi.org/10.1007/s12652-020-02408-x