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

Disadvantage

• The users could suffer physical threats, which are far more harmful than advertisements or spam mails. Therefore, the users face severe challenges when sharing their data in CPSSs.
• Given the challenges and drawbacks on both sides, the privacy preservation problem in publishing records and profiles in CPSSs remains unsolved.
• These works are initially designed for social networks, and do not consider physical data. Therefore, they are not proper for CPSSs.
• They mainly preserve user privacy at single or several locations, which is incapable for long-time behaviors in social networks.

Advantages

• To the best of our knowledge, this is the first work regarding record publishing in CPSSs considering both the privacy on physical profiles and utility on social profiles.
• More specifically, the privacy preservation mechanism on physical profiles mainly aims to perturb the adversary’s knowledge on users’ physical status. It perturbs the probability of a user staying in each status in the physical profile, and reduces the confidence in the frequently appeared status.
• For the utility in social networks, our mechanism maintains the consistency with the original social profile and publishes a maximum number of records.
• It guarantees that users can attract the same type of followers and show their activeness.
• We prove this algorithm can achieve a local maximized result towards published records while following both privacy and utility constraints.
• The experiments on real data sets reveal that our framework outperforms the existing works, and are comparable to the optimal results