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Title: Moving Beyond Set-It-And-Forget-It Privacy Settings on Social Media
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.
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Award ID(s):
1801663 1801644
Publication Date:
Journal Name:
Proceedings of the 26th ACM Conference on Computer and Communications Security (CCS)
Page Range or eLocation-ID:
991 to 1008
Sponsoring Org:
National Science Foundation
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