The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic
Abstract
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 apps, and via an IRB-approved user study with 92 participants. We show that ReCon is accurate, efficient, and identifies a wider range of PII than previous approaches.
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“However, there are methods to limit the amount of sensitive content that is present, e.g., by identifying and filtering personal information or content with restrictive terms of use [11, 58].”
From Extracting Training Data from Large Language Models (2020) · cites this paper as [58]
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Cited by
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727
Security and Privacy Challenges of Large Language Models: A Survey
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Mobile health and privacy: cross sectional study
Tangari, G., Ikram, M., Ijaz, K., Kaafar, M. A., & Berkovsky, S. (2021) · BMJ · doi:10.1136/bmj.n1248 · Cited by 158
Folk Models of Online Behavioral Advertising
Yao, Y., Lo Re, D., & Wang, Y. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998316 · Cited by 69
Analyzing security issues of android mobile health and medical applications
Tangari, G., Ikram, M., Sentana, I. W. B., Ijaz, K., Kaafar, M. A., & Berkovsky, S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab131 · Cited by 38
Metadata
- Authors
- Ren, J., Rao, A., Lindorfer, M., Legout, A., & Choffnes, D.
- Affiliations
- Northeastern University, Boston, USA
- University of Helsinki, Helsinki, Finland
- SBA Research, Vienna, Australia
- Inria, Sophia Antipolis, France
- Venue
- Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services
- Publisher
- ACM
- Published
- 2016-06-20
- Pages
- 361-374
- Cited by
- 132
- References
- 49
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