An efficient data mining technique and privacy preservation model for healthcare data using improved darts game optimizer-based weighted deep neural network and hybrid encryption

Abstract

In recent days, association rule mining techniques have been widely used in healthcare data to provide accurate records that are important to ensure the data privacy. However, making this information public leads to creating attacks on them. In this paper, a secure privacy-preservation scheme for healthcare data is implemented to protect the security of the information for disease prediction in the current healthcare applications. The health data is collected from the benchmark datasets. Initially, the data is encrypted using Fully Homomorphic Encryption and Hyperelliptic Curve Cryptography (FHE-HECC) for the privacy preservation process. This model is developed by combining the Fully Homomorphic Encryption (FHE) and Hyperelliptic Curve Cryptography (HECC). For this encryption, the optimal key is generated using the Improved Darts Game Optimizer (IDGO) leveraging the Darts Game Optimizer (DGO). In the case of data decryption, the above-mentioned cryptography is utilized. The optimally selected key encrypts the data with high security without any breaches. The stored encrypted data is monitored and the disease is recognized using the Weighted Deep Neural Network (W-DNN) method and here, Deep Neural Network (DNN) acts as the fundamental model. Finally, the privacy of health data is preserved and the type of disease is detected by the implemented model. The suggested model attained accuracy of 92.83 which is higher than the existing techniques like GRU with 86.97, RNN with 88.28, LSTM with 91.92, WDNN with 90.10, respectively. The key findings of the suggested approach Proved that it facilitates effective treatment to the patient.