An Effective Paillier Encryption for Health Data with Cloud Storage and Prediction Using RNN
Sahila Devi R, Govinda Patil, D. Srinivasa Rao, Jitendra Choudhary, Sriram Gopalam, Priyanka Parmar · International Journal of Electrical and Electronics Engineering · 2024
An emerging technology in data analytics, Cloud Computing (CC) is used to store, retrieve, and distribute data in a dispersed setting. Both individuals and businesses save their data on cloud servers. However, data privacy and security have become a rising problem that limits the organization from employing cloud services. In order to overcome that problem, the Paillier encryption method is proposed to offer safety for healthcare cloud data. Creating a pair of keys offers a comparatively easier method to ward against attacks and safeguard data confidentiality. Following data selection, the Paillier methodology is used to encrypt the input. The encoded health data were then kept in cloud environments, where they provided better read-write storage operations. The Paillier method is used in the data decryption procedure to get the data from the cloud environment. A Recurrent Neural Network (RNN) classifier is employed in healthcare diagnosis to categorize and forecast a patient’s ailment, after which the diagnosis is communicated to the patient and physician. The proposed work uses Python software, and a comparative analysis is conducted. An effective prediction using RNN showcases an accuracy of 96.4% with a better performance matrix. After classification, the outcomes are conveyed to patients and doctors for treatment.