Privacy in medical records while data sharing is a major concern for distributed learning models. The dataset generated and shared via Electronic Medical Records (EMR) consist of sensitive medical information such as patient identify and experts’ recommendations, and causes setbacks in training larger models, dataset augmentation and polluting datasets with recursive attributes. The information processing and de-identification is proposed in this article to preserve and enhance the privacy of EMR. The proposed technique is termed as PrEGAN, i.e., Privacy Enhanced Generative Adversarial Network (GAN) for EMR data training and realistic mapping. The proposed model generates and discriminates the ground truth with generated mask via a computation of loss function for de-identification or removal of personal linked/connected data in the records networks. The objective is to generate the mask of EMR, which is realistic and similar to the ground truth. The model is trained and validated with two distinguished discriminators, the CNN based discriminator is used for medical images, whereas Neural Networks are used for textural data generator. The experimental results demonstrate a higher degree of data privacy and de-identification in EMR with 88.32% accuracy in predicting and eliminating via RoI and loss function.