Abstract
A user’s trajectory can be maliciously monitored by adversaries when they share the positions in location-aware social networking applications which require users to update their own locations continuously. An adversary infers user’s locations from the trajectories, and gleans user’s private information through them via location-aware social networking applications and public available geographic data. In this paper, we propose a user proprietary obfuscate system to suit situations for position sharing and location privacy preserving in location-aware social network. Users transform the public available geographic data into personal obfuscate region maps with pre-defined profile to prevent the location leaking in stationary status. Our obfuscation with size restricted regions method tunes user’s transformed locations fitting into natural movement and prevents unreasonable snapshot locations been recorded in the trajectory.
Highlights
The users’ physical locations are widely adopted by applications installed in mobile devices as fundamental information to enhance the services
Social networking application is one of the popular mobile applications involved with location that Pew Research showed 28% of the mobile users utilize the application on a typical day basis [1]
We propose utilizing the obfuscate region map to act for user’s physical location when users are in stationary status, and obfuscation based with restricted the region size solution to prevent the locations leaking during movement
Summary
The users’ physical locations are widely adopted by applications installed in mobile devices as fundamental information to enhance the services. The approach suits in the check-in service but not location sharing in the location-aware social networking, since users attempt to connect nearby strangers. While they’re moving with velocity, the movement shall sustain undisclosed all the time Another location privacy preserving approach by Damiani et al proposed a spatial obfuscating system [4], based on user’s pre-defined sensitive semantic model, and user’s concerned locations will not be disclosed when they move into the sensitive locations. We propose a system to transform wider space of actual geographic data into correlated regions map in advance, and use regions’ coordinates to substitute for actual ones It conquers the two keynotes privacy concerns when sharing positions in location-aware social network applications
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