Securing healthcare data in mobile edge computing: a hybrid deep learning framework for privacy and anomaly detection

Data have always been a top goal in achieving intelligent healthcare in a smart city, especially with the rapidly growing deployment of the Internet of Things and connectivity. The significant challenges that remain in the evolving landscape of mobile edge computing and smart healthcare are ensuring data privacy and detecting anomalies in sensitive medical information. The research proposes a novel technique, namely a lightweight differential extreme gradient reinforcement neural network with stochastic vector autoencoder-based adversarial Bayes convolutional regression (LDExGRNN_SVA-AdBCR), which combines multiple machine learning strategies to preserve privacy and detect anomalies in healthcare data. In this approach, a lightweight differential extreme gradient reinforcement neural network that integrates differential privacy techniques with XGBoost and deep Q-learning reinforcement strategies is utilized to achieve privacy preservation. Anomaly prediction is carried out using a stochastic vector autoencoder-based adversarial Bayes convolutional regression model that combines a stochastic vector autoencoder with multiple classifiers and regressors, including Naïve Bayes, support vector machines, generative adversarial networks, and vector autoregression. To enhance detection robustness, outputs from multiple classifiers and the GAN discriminator score are integrated using a majority voting mechanism. This ensemble fusion boosts reliability in detecting diverse anomalies across healthcare datasets. The experimental analysis is evaluated on various healthcare datasets in terms of privacy rate (98.1%), prediction accuracy (98.75%), scalability (98.3%), and F1-score (98.34%). Overall, the LDExGRNN_SVA-AdBCR method provides a scalable solution for detecting anomalies in MEC environments and securing healthcare data.