Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Abstract

Due to the close correlation with individual’s physical features and status, the adoption of cyber-physical social systems (CPSSs) has been inevitably hindered by users’ privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results toward real datasets reveals that the performance is comparable to the global optimal one.