Buschek, D.D.BuschekBader, M.M.BaderZezschwitz, E. vonE. vonZezschwitzLuca, A.E. deA.E. deLuca2022-03-142022-03-142015https://publica.fraunhofer.de/handle/publica/40445310.1007/978-3-319-22668-2_33Tagging photos with privacy-related labels, such as ""myself"", ""friends"" or ""public"", allows users to selectively display pictures appropriate in the current situation (e.g. on the bus) or for specific groups (e.g. in a social network). However, manual labelling is time-consuming or not feasible for large collections. Therefore, we present an approach to automatically assign photos to privacy classes. We further demonstrate a study method to gather relevant image data without violating participants' privacy. In a field study with 16 participants, each user assigned 150 personal photos to self-defined privacy classes. Based on this data, we show that a machine learning approach extracting easily available metadata and visual features can assign photos to user-defined privacy classes with a mean accuracy of 79.38 %.en004conference paper