Secure Data Transmission in Healthcare Internet of Things Using Bayesian Deep Q Neural Network with Privacy Preservation Authentication Scheme - M. Lorate Shiny (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The integration of the Internet of Things (IoT) in the healthcare industry has led to the development of efficient IoT-based healthcare systems for real-time patient monitoring, with the ability to store and process large amounts of data. However, the sharing of sensitive patient information, such as health status and device usage, over the cloud raises security concerns. To address these issues, cryptographic methods and deep learning models have been used to provide secure data transmission and anomaly detection. In this paper, a privacy-preserving deep learning model called the Bayesian Deep Q neural network (BDQNN) with Ciphertext-attribute based policy encryption (CP-ABPE) has been proposed to protect transmitted data from external threats and reduce communication overhead in an IoT-based healthcare system. The proposed model was evaluated through simulation experiments and demonstrated superior performance compared to traditional methods, with an accuracy of 98.3%, sensitivity of 94.2%, specificity of 96.9%, precision of 95.9%, communication overhead of 68.1%, encryption time of 59.4ms, and decryption time of 60.2ms.