With the growing popularity of social networking platforms, people are sharing personal photos online at an unprecedented rate. Photographs capture a plethora of information, some of which are incidental and not relevant to the subject matter. Such incidental information could be privacy sensitive for the owner of a photo and/or people appearing in it. A straightforward solution to this problem is to obscure sensitive image content using image filters (e.g., blurring). In the context of social media, however, an additional challenge is to retain enough utility for the viewers. In this paper, we explore methods to keep sensitive information private while preserving image utility. Existing research on privacy-enhancing image filters predominately focuses on obscuring faces or lack a systematic study of how filters affect image utility. We study the effectiveness and user acceptance of eleven different filters, applied to various privacy-sensitive attributes of an image.