Christopher Tran
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Christopher Tran has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Blase Ur
University of Chicago, Chicago, Illinois, USA
1 shared publication - Chris Kanich
University of Illinois at Chicago
1 shared publication - Elena Zheleva
University of Illinois Chicago, Chicago, USA
1 shared publication - Günce Su Yilmaz
University of Chicago, Chicago, IL, USA
1 shared publication - Mainack Mondal
IIT Kharagpur
1 shared publication - Michael Tang
University of Chicago, Chicago, IL, USA
1 shared publication
Latest Publications
- Open Access
- 6 November 2019
- Published by Association for Computing Machinery (ACM)
- p. 991-1008
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. CCS CONCEPTS • Security and privacy → Usability in security and privacy.
- 6 November 2019
- Published by Association for Computing Machinery (ACM)
- p. 991-1008
Open AccessMoving 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. CCS CONCEPTS • Security and privacy → Usability in security and privacy.