This study deals with the design and implementation of a learning model to prevent the leakage of sensitive information of the original data during the learning process of a model that generates high-quality data that is difficult to distinguish from the original using a generative adversarial network (GAN). Existing European General Personal Information Protection Act (GDPR) and domestic Personal Information Protection Act had limitations in that it was difficult to effectively respond to the privacy leakage occurring in the characteristics of the Generative AI model and the learning method. In order to improve this problem, AI Act that applies differential regulations for each risk level of the system was introduced. Therefore, this study aims to introduce personal information enhancement technology (PETs) into the GAN model design process to promote a balance between the usefulness of the learning model and privacy enhancement, and to propose a new P-GAN model that can quantitatively evaluate the learning results. In this paper, by describing the design principles and implementation results of the proposed model in detail, we seek the direction of improvement of the future generative model's usefulness and privacy protection function enhancement system.