  Images convey a broad spectrum of personal information. If such images are
shared on social media platforms, this personal information is leaked which
conflicts with the privacy of depicted persons. Therefore, we aim for automated
approaches to redact such private information and thereby protect privacy of
the individual. By conducting a user study we find that obfuscating the image
regions related to the private information leads to privacy while retaining
utility of the images. Moreover, by varying the size of the regions different
privacy-utility trade-offs can be achieved. Our findings argue for a "redaction
by segmentation" paradigm. Hence, we propose the first sizable dataset of
private images "in the wild" annotated with pixel and instance level labels
across a broad range of privacy classes. We present the first model for
automatic redaction of diverse private information.
