Authors
Jingjing Ren, Ashwin Rao, Martina Lindorfer, Arnaud Legout, David Choffnes
Publication date
2016/6/20
Book
Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services
Pages
361-374
Description
It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. We evaluate ReCon's effectiveness with measurements from controlled experiments using leaks from the 100 most popular iOS, Android, and Windows Phone …
Total citations
20152016201720182019202020212022202320244152633443836463614
Scholar articles
J Ren, A Rao, M Lindorfer, A Legout, D Choffnes - Proceedings of the 14th Annual International …, 2016