In recent years, personal and medical data collected through mobile apps has become a useful data source for researchers. Platforms like Apple ResearchKit try to make it as easy as possible for non-experts to set up such data collection campaigns. However, since the collected data is sensitive, it must be well protected. Methods that provide technical privacy guarantees often limit the usefulness of the data and results. In this paper, we model and analyze mobile data donation to better understand the requirements that must be fulfilled by privacy-preserving approaches. To this end, we give an overview of the functionalities researchers require from data donation apps by analyzing existing apps. We also create a model of the current practice and analyze it using the LINDDUN privacy framework to identify privacy threats.