Abstract
Over the recent years mobile devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for commons. However, the privacy threats along with the intimate use of smartphone has become a primary concern. A significant number of methods for persevering privacy against smartphone usage data were also proposed in recent years. The prior research mainly focuses on the privacy leakages by motion sensors, microphones, and GPS trajectories. In this paper, we report another privacy threats by analyzing a collected trace of Wi-Fi signals (referred to as Wi-Fi logs) observed by a smartphone. Such privacy leakage is neglected in the past, as Wi-Fi log data are generally considered to be less sensitive compared with GPS or microphone data. However, in this study, we show that by analyzing the Wi-Fi logs, an adversary can readily reveal many about a smartphone holder, such as occupations, moving patterns, or even user identity. To raise the concerns on this privacy leakage, we design experiments and propose a simple scheme to analyze the Wi-Fi log traces collected by recruited participants. The goal of the scheme is not to design a perfect scheme for discovering user related information but to clearly illustrate the existence and easy identification of privacy-revealing vulnerabilities in Wi-Fi trace logs. The experiment results demonstrate that the privacy can be leaked by Wi-Fi trace logs, which can be readily collected by any app requesting innocence permissions. The experiment results are alarming and may motivate the need to improve the privacy concerns by developing better privacy preserving mechanisms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liang Cai and Hao Chen. Touchlogger: Inferring keystrokes on touch screen from smartphone motion. HotSec, 11:9–9, 2011.
2. Ningning Cheng, Xinlei Oscar Wang, Wei Cheng, Prasant Mohapatra, and Aruna Seneviratne. Characterizing privacy leakage of public wi_ networks for users on travel. In Proceedings of the IEEE INFOCOM 2013, Turin, Italy, April 14-19, 2013, pages 2769–2777. IEEE, 2013.
Nathan Eagle and Alex Sandy Pentland. Reality mining: sensing complex social systems. Personal and ubiquitous computing, 10(4):255–268, 2006.
Kassem Fawaz and Kang G Shin. Location privacy protection for smartphone users. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pages 239–250. ACM, 2014.
5. Saikat Guha, Mudit Jain, and Venkata N Padmanabhan. Koi: A location-privacy platform for smartphone apps. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, pages 14–14. USENIX Association, 2012.
Hong Li, Limin Sun, Haojin Zhu, Xiang Lu, and Xiuzhen Cheng. Achieving privacy preservation in wi_ _ngerprint-based localization. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pages 2337–2345. IEEE, 2014.
7. Emmanuel Owusu, Jun Han, Sauvik Das, Adrian Perrig, and Joy Zhang. Accessory: password inference using accelerometers on smartphones. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, page 9. ACM, 2012.
Roman Schlegel, Kehuan Zhang, Xiao-yong Zhou, Mehool Intwala, Apu Kapadia, and XiaoFeng Wang. Soundcomber: A stealthy and context-aware sound trojan for smartphones. In NDSS, volume 11, pages 17–33, 2011.
Yu Zheng, Xing Xie, and Wei-Ying Ma. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2):32–39, 2010.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hsu, CY., Yang, SE., Chen, HY., Leu, FY., Fan, YC. (2017). Inferring Smartphone User Demographics from Wi-Fi trace Logs: A Study of Users’ Privacy Concerns. In: Barolli, L., Xhafa, F., Yim, K. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-49106-6_74
Download citation
DOI: https://doi.org/10.1007/978-3-319-49106-6_74
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49105-9
Online ISBN: 978-3-319-49106-6
eBook Packages: EngineeringEngineering (R0)