Privacy-Preserving Medical Data Generation Using Adversarial Learning

Outstanding performance has been observed in a number of real-world applications such as speech processing and image classification using deep learning models. However, developing these kinds of models in sensitive domains such as healthcare usually necessitates dealing with a specific level of privacy challenges which provide unique concerns. For managing such privacy concerns, a practical method might involve generating feasible synthetic data that not only provides acceptable data quality but also helps to improve the efficiency of the model. Synthetic Data Generation (SDG) innately includes Generative Adversarial Networks (GANs) that have drawn significant interest in this field as a result of their achievement in various other research areas. In the study, a framework safeguarding privacy, which employs R ' enyi Differential Privacy along with Generative Adversarial Networks and a Variational Autoencoder (RDP-VAEGAN), is introduced. This approach is evaluated and contrasted with other top-tier models having identical privacy constraints, utilizing both unsupervised and supervised methods on two medical datasets that are publicly accessible.