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AppIntent: analyzing sensitive data transmission in android for privacy leakage detection
Abstract
Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.
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Citations
Cited by
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic
Ren, J., Rao, A., Lindorfer, M., Legout, A., & Choffnes, D. (2016) · Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2906388.2906392 · Cited by 132
Conceptual framework for the security of mobile health applications on Android platform
Hussain, M., Zaidan, A. A., Zidan, B. B., Iqbal, S., Ahmed, M. M., Albahri, O. S., & Albahri, A. S. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2018.03.005 · Cited by 122
Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis
Continella, A., Fratantonio, Y., Lindorfer, M., Puccetti, A., Zand, A., Kruegel, C., & Vigna, G. (2017) · Proceedings 2017 Network and Distributed System Security Symposium · doi:10.14722/ndss.2017.23465 · Cited by 90
PrivacyStreams: Enabling Transparency in Personal Data Processing for Mobile Apps
Li, Y., Chen, F., Li, T. J.-J., Guo, Y., Huang, G., Fredrikson, M., Agarwal, Y., & Hong, J. I. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3130941 · Cited by 39
EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage
Liao, Z., Mo, L., Xu, C., Kang, M., Zhang, J., Xiao, C., Tian, Y., Li, B., & Sun, H. (2024) · arXiv · doi:10.48550/arxiv.2409.11295 · Cited by 1
“…sensitive PII, such as phone numbers, email addresses, and credit card details; while the web security community has long studied privacy issues (Yang et al., 2013; Li et al., 2015; Wang et al., 2019; VirusTotal, 2023), the emergence of generalist web agents poses new risks of user privacy…”
Metadata
- Authors
- Yang, Z., Yang, M., Zhang, Y., Gu, G., Ning, P., & Wang, X. S.
- Venue
- Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS '13
- Publisher
- ACM Press
- Published
- 2013
- Pages
- 1043-1054
- OpenAlex
- W2085577046
- Cited by
- 348
- References
- 0
- FWCI
- 36.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yang, Z., Yang, M., Zhang, Y., Gu, G., Ning, P., & Wang, X. S. (2013). AppIntent: analyzing sensitive data transmission in android for privacy leakage detection. Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS '13. https://doi.org/10.1145/2508859.2516676
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