To accommodate different perspectives on the distinctiveness of private and utility features across user groups, the problem formulation can be adapted by introducing customizable parameters that allow for varying levels of differentiation between private and utility attributes. This customization can be achieved by incorporating adjustable weights or constraints in the privacy mechanism training process, enabling users to specify the importance of privacy versus utility based on their specific requirements. By providing flexibility in defining the tradeoff between privacy and utility, the problem formulation can cater to diverse philosophical viewpoints and privacy preferences within different user groups. Additionally, incorporating a mechanism for users to input their preferences or constraints regarding the handling of private and utility features can enhance the adaptability and inclusivity of the privacy-utility tradeoff optimization process.