In light of the recent technological advancements, our society has evolved into a prolific source of data generation, accompanied by the widespread use of machine learning algorithms, particularly deep neural networks. However, these algorithms rely on substantial datasets which often contain sensitive and private information.
Within this context, generative models have emerged to create synthetic samples across various domains. Ideally, these models should prevent the exposure of individual-specific information from the training data.