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When users post on social media, they protect their privacy by choosing an access control setting that is rarely revisited. Changes in users' lives and relationships, as well as social media platforms themselves, can cause mismatches between a post's active privacy setting and the desired setting. The importance of managing this setting combined with the high volume of potential friend-post pairs needing evaluation necessitate a semi-automated approach. We attack this problem through a combination of a user study and the development of automated inference of potentially mismatched privacy settings. A total of 78 Facebook users reevaluated the privacy settings for five of their Facebook posts, also indicating whether a selection of friends should be able to access each post. They also explained their decision. With this user data, we designed a classifier to identify posts with currently incorrect sharing settings. This classifier shows a 317% improvement over a baseline classifier based on friend interaction. We also find that many of the most useful features can be collected without user intervention, and we identify directions for improving the classifier's accuracy. 

Online archives, including social media and cloud storage, store vast troves of personal data accumulated over many years. Recent work suggests that users feel the need to retrospectively manage security and privacy for this huge volume of content. However, few mechanisms and systems help these users complete this daunting task. To that end, we propose the creation of usable retrospective data management mechanisms, outlining our vision for a possible architecture to address this challenge.