Location Privacy Protection for Smartphone Users

A multitude of applications (apps), not necessarily location-based services, running on mobile devices, are known to collect the users’ location information. Most of them are free apps that use location information to build the users’ profiles based on their whereabouts and movements. Service providers as well as analytics and advertising (A&A) agencies then utilize these profiles to tailor their services to different users. Consequently, service providers and A&A agencies collect and maintain location traces of users who run different apps on their mobile devices.


Faculty: Kang G. Shin
Current Students: Kassem Fawaz and Huan Feng
Previous Students: Xiaoen Ju, Xin Hu, and Zhigang Chen

Anatomization and Protection of Mobile Apps’ Location Privacy Threats

Abstract – Mobile users are becoming increasingly aware of the privacy threats resulting from apps’ access of their location. Few of the solutions proposed thus far to mitigate these threats have been deployed as they require either app or platform modifications. Mobile operating systems (OSes) also provide users with location access controls. In this paper, we analyze the efficacy of these controls in combating the location-privacy threats. For this analysis, we conducted the first location measurement campaign of its kind, analyzing more than 1000 free apps from Google Play and collecting detailed usage of location by more than 400 location-aware apps and 70 Advertisement and Analytics (A&A) libraries from more than 100 participants over a period ranging from 1 week to 1 year. Surprisingly, 70% of the apps and the A&A libraries pose considerable profiling threats even when they sporadically access the user’s location. Existing OS controls are found ineffective and inefficient in mitigating these threats, and a finer-grained location access control is thus needed. To meet this need, we propose LP-Doctor, a light-weight user-level tool that allows Android users to effectively utilize the OS’s location access controls while maintaining the required app’s functionality as our user study (with 227 participants) shows.

App categorization according to threat levels, location requirements, and location access patterns.

App categorization according to threat levels, location requirements, and location access patterns.

LP-Guardian: Location privacy protection for Android users

Abstract – As smartphones are increasingly used to run apps that provide users with location-based services, the users’ location privacy has become a major concern. Existing solutions to this concern are deficient in terms of practicality, efficiency, and effectiveness. To address this problem, we design, implement, and evaluate LP-Guardian, a novel and comprehensive framework for location privacy protection for Android smartphone users. LP-Guardian overcomes the shortcomings of existing approaches by addressing the tracking, profiling, and identification threats while maintaining app functionality. We have implemented and evaluated LP-Guardian on Android 4.3.1. Our evaluation results show that LP-Guardian effectively thwarts the privacy threats, without deteriorating the user’s experience (less than 10% overhead in delay and energy). Also, LP-Guardian’s privacy protection is shown to be achieved at a tolerable loss in app functionality.


LP-Guardian’s architecture and interactions of its components

LISA: Location Information ScrAmbler for Privacy Protection on Smartphones

Abstract – As use of location-based services (LBSs) is becoming increasingly prevalent, mobile users are more and more enticed to reveal their locations, which may be exploited by attackers to infer the points of interest (POIs) the users visit and then their privacy information. We propose a novel approach to the protection of a user’s location privacy based on unobservability, preventing the attackers from relating any particular POI to the user’s current location. We design, implement, and evaluate a privacyprotection system, called the Location Information ScrAmbler (LISA) which protects the user’s location privacy by adjusting the location noise and hence, the uncertainty of associating his location with any POI, while conserving resources (especially battery energy) on mobile devices. By protecting location privacy locally on each mobile user’s device, LISA eliminates the reliance on the trusted third-party servers required by most existing approaches. Therefore, it not only avoids the vulnerability of a single point of failure, but also facilitates the deployment of LBSs. Our evaluation of LISA using real-world users’ traces demonstrates its efficacy and efficiency.


Privacy protection engine of LISA



Currently, this project is supported by the National Science Foundation under Grant CNS-1114837.


  • Kassem Fawaz, Huan Feng, and Kang G. Shin, Anatomization and Protection of Mobile Apps’ Location Privacy Threats, The 24th USENIX Security Symposium (Sec ‘15), August 12-14, 2015, Washington, D.C., USA. PDF pdf
  • Kassem Fawaz and Kang G. Shin, Location Privacy Protection for Smartphone UsersThe 21st ACM Conference on Computer and Communications Security (CCS’14), Nov. 3-7, 2014, Scottsdale, Arizona, USA. PDFpdf
  • Zhigang Chen, Xin Hu, Xiaoen Ju, Kang Shin. LISA: Location Information ScrAmbler for Privacy Protection on Smartphones, IEEE Conference on Communications and Network Security (CNS2013)PDF pdf
  • Kang G. Shin, Xiaoen Ju, Zhigang Chen, and Xin Hu. Privacy protection for users of location-based services, IEEE Wireless Communications Magazine, vol. 19, no. 1, pp. 30-39, February, 2012, link.