While smartphones and mobile apps have been an essential part of our lives, privacy is a serious concern. Previous mobile privacy related research efforts have largely focused on predefined known sources managed by smartphones. Sensitive user inputs through UI (User Interface), another information source that may contain a lot of sensitive information, have been mostly neglected. In this paper, we examine the possibility of scalably detecting sensitive user inputs from mobile apps. In particular, we design and implement SUPOR, a novel static analysis tool that automatically examines the UIs to identify sensitive user inputs containing critical user data, such as user credentials, finance, and medical data. SUPOR enables existing privacy analysis approaches to be applied on sensitive user inputs as well. To demonstrate the usefulness of SUPOR, we build a system that detects privacy disclosures of sensitive user inputs by combining SUPOR with off-the-shelf static taint analysis We apply the system to 16,000 popular Android apps, and conduct a measurement study on the privacy disclosures. SUPOR achieves an average precision of 97.3% and an average recall of 97.3% for sensitive user input identification. SUPOR finds 355 apps with privacy disclosures and the false positive rate is 8.7%. We discover interesting cases related to national ID, username/password, credit card and health information.
|Original language||English (US)|
|Title of host publication||Proceedings of the 24th USENIX Security Symposium|
|Number of pages||16|
|State||Published - 2015|
|Event||24th USENIX Security Symposium - Washington, United States|
Duration: Aug 12 2015 → Aug 14 2015
|Name||Proceedings of the 24th USENIX Security Symposium|
|Conference||24th USENIX Security Symposium|
|Period||8/12/15 → 8/14/15|
Bibliographical noteFunding Information:
The authors would like to thank the anonymous reviewers for their insightful comments that helped improve the presentation of this paper. Jianjun Huang and Xiangyu Zhang are supported, in part, by National Science Foundation (NSF) under grants 0845870, 1320444, 1320326 and 1409668. Any opinions, findings, and conclusions or recommendations in this paper are those of the authors and do not necessarily reflect the views of NSF.
© 2015 Proceedings of the 24th USENIX Security Symposium. All rights reserved.