A new approach called FusionDP is making waves in the field of privacy-preserving machine learning by focusing on differentially private learning for partially sensitive features. This is particularly important as it allows for the protection of sensitive data, like demographic information in ICU settings, while still utilizing less sensitive data effectively. By not enforcing privacy on all features, FusionDP aims to strike a balance between data utility and privacy, which is crucial for real-world applications. This innovation could significantly enhance how sensitive data is handled in various sectors, ensuring better privacy without sacrificing the quality of machine learning models.